In Part 1, we introduced Maple Industrial Supply.

Maple's sales increased from $4.2 million to $4.8 million. Its EBITDA increased from $420,000 to $480,000.

Yet Maple has used $850,000 of its $900,000 operating line and is asking a supplier to double its credit limit.

If sales and EBITDA increased, why is Maple still looking for more credit?

Start with one sale

Suppose Maple sells $100,000 of inventory to a customer on 60-day terms. The inventory cost Maple $80,000.

Maple records the following:

Maple has recorded a $20,000 profit. But the customer has not paid yet.

The sale increased Maple's revenue and profit, but it did not immediately increase Maple's cash.

The income statement records the sale when it is earned. Cash flow reflects when the money is actually received or paid.

Now look at Maple's full year

During the year, Maple's accounts receivable increased from $700,000 to $1.1 million.

$1,100,000 - $700,000 = $400,000

This means Maple ended the year with $400,000 more tied up in customer accounts than it had at the beginning of the year.

Inventory also increased:

$900,000 - $650,000 = $250,000

Maple therefore had another $250,000 tied up in inventory. Together, receivables and inventory absorbed:

$400,000 + $250,000 = $650,000

Who helped finance the increase

Maple's accounts payable increased from $500,000 to $650,000.

$650,000 - $500,000 = $150,000

By carrying $150,000 more in accounts payable, Maple's suppliers financed part of the increase in receivables and inventory.

The remaining amount was:

$650,000 - $150,000 = $500,000

Maple therefore had to finance approximately $500,000 of the increase from other sources.

Compare that with EBITDA

Maple reported EBITDA of $480,000. A simplified comparison looks like this:

Using only these figures, Maple's receivables and inventory absorbed more cash than its EBITDA provided.

That helps explain why Maple could report higher sales and EBITDA while also using most of its operating line and requesting more supplier credit.

This is not Maple's complete operating cash flow. We have not included interest, taxes, capital expenditures, debt repayments or other balance-sheet changes.

It is a simplified calculation designed to show one thing: profitability does not tell us how quickly earnings are being converted into cash.

Working capital is a different calculation

Working capital is calculated at a particular date:

Current assets - current liabilities

For example:

Company A has positive working capital of $300,000 and negative EBITDA of $100,000. Both statements can be true.

Working capital describes the company's financial position at one date. EBITDA describes its operating performance over a period.

Now consider Company B:

Company B has negative working capital of $200,000 and positive EBITDA of $300,000. Again, both statements can be true.

The company may be profitable but have substantial short-term obligations. Alternatively, it may operate a business that collects from customers before paying suppliers.

The figures create questions. They do not provide the complete answer.

What should the credit professional learn

Three calculations are telling us different things:

·        EBITDA helps describe operating earnings.

·        Working capital compares current assets with current liabilities at one date.

·        Changes in receivables, inventory and payables help explain why cash increased or decreased during a period.

None should be interpreted alone.

For Maple, the issue is not simply whether EBITDA is positive. It is whether Maple can convert its sales, receivables and inventory into cash before its bank and supplier obligations become due.

How much cash is tied up, how long will it remain tied up, and who is financing the gap?

In Part 3, we will examine that timing through Maple's cash-conversion cycle.

A commercial banker and a B2B credit professional may assess the same business at the same time.

The banker may be reviewing its operating line. The credit professional may be deciding whether to increase its trade credit limit.

Both are assessing creditworthiness—but they are not necessarily making the same decision.

I asked AI to compare their perspectives using a simplified case.

Meet Maple Industrial Supply

Maple Industrial Supply has asked one of its suppliers to increase its credit limit from $75,000 to $150,000, with payment terms of 60 days.

At approximately the same time, Maple’s bank is reviewing its $900,000 operating line.

Maple provides the following year-end information:

Financial information	Previous year	Current year
Sales	$4,200,000	$4,800,000
EBITDA	$420,000	$480,000
Accounts receivable	$700,000	$1,100,000
Inventory	$650,000	$900,000
Accounts payable	$500,000	$650,000

We also know that:

• Maple’s customers pay in an average of 75 days;
• $850,000 of its $900,000 bank line is currently used; and
• Maple is requesting 60-day terms from its supplier.

Would the banker and the B2B credit professional assess this information in the same way?

What they have in common

Both need to answer a fundamental question:

Where will the cash to repay the credit come from?

For a healthy operating business, repayment will normally come from cash generated through its operations.

The banker needs to understand how Maple will repay amounts borrowed under its operating line. The credit professional needs to determine how Maple will pay the supplier’s invoices when they become due.

Both may consider:

• sales and profitability;
• cash flow;
• receivables and inventory;
• existing obligations;
• payment history;
• customer concentration; and
• conditions affecting Maple’s industry.

Both will also want to know what could delay or interrupt the expected repayment.

Where their perspectives differ

The banker is evaluating Maple’s overall borrowing requirement and its ability to repay the operating line. Depending on the lending agreement, the bank may receive regular financial reporting, hold security, impose covenants and establish which assets support the facility.

The B2B credit professional is deciding whether to deliver additional goods or services and wait for payment. The supplier may receive less financial information and may not have the same security as the bank.

But the supplier may have information the banker does not see as quickly.

Changes in payment behaviour, requests for extended terms, unusual purchasing activity, deductions, disputes and broken payment commitments can reveal how Maple is managing its trade obligations.

The banker and supplier are therefore looking at the same business through different exposures and sources of information.

What do we see in Maple’s numbers?

There are favourable developments:

• sales increased;
• EBITDA increased; and
• the business appears to be growing.

There are also developments requiring explanation:

• receivables increased;
• inventory increased;
• accounts payable increased;
• most of the operating line is already in use; and
• Maple wants the supplier to double its credit exposure.

None of these facts, considered separately, proves that Maple is financially weak.

Receivables and inventory may increase because a healthy business is growing. Greater use of an operating line may be consistent with that growth. An increase in payables may reflect higher purchasing volume.

The same changes could also indicate slower collections, excess inventory or increasing dependence on borrowed money and supplier credit.

The figures tell us what changed. They do not yet tell us why it changed.

Could the banker and supplier reach different decisions?

Yes.

The bank and the supplier have different exposures, repayment arrangements, information and protections. One may approve credit while the other declines it or imposes additional conditions.

A bank might remain comfortable because its lending is supported by eligible assets, security and financial reporting.

A supplier might be less comfortable because it is being asked to double its exposure without receiving comparable protection.

The reverse is also possible. A supplier may approve a controlled amount based on strong payment experience and direct knowledge of the customer, even if the bank is unwilling to increase its facility.

Neither decision automatically proves that the other is wrong.

The question is not simply:

Would the bank lend to Maple?

For the B2B credit professional, the better question is:

Given our particular exposure, terms, information and protection, what evidence shows that Maple can and will pay us when our invoices become due?

In Part 2, we will examine Maple’s working-capital changes and ask a more difficult question: If sales and EBITDA increased, where did the cash go?

Moving from understanding bond mechanics to interpreting credit signals

I ended the first article in this series with a question:

What might the capital markets be telling me about a customer's ability to pay tomorrow that I can't yet see from how they're paying me today?

It came out of my attempt to understand bonds using ChatGPT as a tutor.

By the end of that exercise, I understood the basic relationship:

Bond prices go up → yields go down.

Bond prices go down → yields go up.

I also learned that a company's bond yield can provide another signal about how investors perceive its credit risk.

But that raised a more difficult question.

If a customer's bond yield suddenly rises, does that mean the company has become riskier?

Not necessarily.

So I went back to my AI tutor.

A rising yield doesn't tell me enough

Consider a customer whose bonds yielded 6% six months ago and now yield 9%.

My first reaction as a credit professional might be:

Something is wrong. Investors are demanding much more return to lend to this company.

Maybe.

But I am missing an important piece of information:

What happened to the rest of the bond market?

Suppose the comparable government yield also moved:

Government bond: 4% → 7%

Customer's bond: 6% → 9%

The customer's borrowing cost has certainly increased. That matters, especially if it needs to refinance.

But notice the difference between the two yields.

Six months ago: 6% - 4% = 2%

Today: 9% - 7% = 2%

The credit spread hasn't changed.

Investors are still demanding roughly the same additional return for taking this company's risk rather than government risk.

The market moved.

That is quite different from the market specifically becoming more worried about my customer.

Now change one number

Suppose instead:

Government bond: 4% → 3%

Customer's bond: 6% → 9%

Now something very different has happened.

The credit spread moved from:

2% → 6%

Investors are accepting less return to hold government debt while demanding much more to hold my customer's debt.

That doesn't prove the company is heading for financial trouble.

But if I'm extending trade credit to this business, I want to know why the market is suddenly demanding so much more compensation for its risk.

Perhaps leverage increased. Perhaps earnings expectations deteriorated. Perhaps free cash flow weakened. Perhaps there is a large debt maturity approaching. Perhaps an acquisition increased financial risk. Or perhaps the market has overreacted.

The bond market isn't making my credit decision for me.

It's giving me a reason to investigate.

Then came the recession question

ChatGPT tested me on another scenario.

A recession begins. Investors become nervous and rush into U.S. Treasuries as a safe haven.

What happens?

I correctly reasoned that investors would flock to Treasuries.

Then I got the next part wrong.

I said Treasury prices would fall and yields would rise.

But investors are buying Treasuries.

Demand ↑ → prices ↑ → yields ↓

At the same time, those investors could be selling riskier corporate bonds.

So we could have:

Treasury yields ↓

while:

Corporate bond yields ↑

That widening gap can tell us something about how investors' appetite for corporate credit risk is changing.

And it gives the credit professional some additional context.

The customer may still be paying me on time

This is perhaps the most important part.

Suppose my customer continues paying invoices within terms.

Its latest financial statements look reasonably good.

But its bonds are falling in price, its credit spread is widening and it has a substantial amount of debt coming due next year.

Which signal should I believe?

That's probably the wrong question.

I should consider all of them.

Payment history tells me how the customer has been paying.

Financial statements help me understand its financial position and past performance.

Cash-flow analysis helps me understand whether the business is generating the cash necessary to support its obligations.

And capital markets may tell me something about what investors expect next.

The value is in putting those signals together.

Refinancing can change the picture quickly

Consider a company with:

$500 million in cash

$2 billion in debt

negative free cash flow

and:

$800 million of debt maturing next year.

Looking only at the $500 million cash balance might give me some comfort.

But where will the company get the money to deal with that $800 million maturity?

Perhaps it intends to refinance it.

That may have been relatively easy when investors were willing to lend at 5%.

What if they now demand 10%?

Or what if financing becomes difficult to obtain at almost any reasonable price?

The company's factories didn't suddenly disappear.

Its customers may still be buying.

It may still be paying my invoices.

But its access to capital has changed.

For a business dependent on refinancing, that can eventually become a liquidity problem.

This changes the credit question

We often ask:

Is this company profitable?

We also ask:

Does it have enough liquidity?

But understanding the bond market added another question to my list:

How dependent is this business on continuing access to external financing—and what happens if that financing becomes significantly more expensive or harder to obtain?

That brings me back to where Part 1 ended.

The bond market isn't a crystal ball.

A widening credit spread doesn't tell me to reduce a credit limit.

A falling bond price doesn't tell me a customer will default.

And investors can certainly be wrong.

But these can be forward-looking signals that deserve attention alongside financial statements, payment behaviour, cash flow and everything else we use to assess creditworthiness.

Perhaps the better question isn't:

"What is the bond market telling me?"

It's:

"What is the bond market telling me to investigate?"

And that's where we move from understanding bonds to using the information as credit professionals.

Ready for Round 2?

The first quiz tested whether you understood how bonds work.

Round 2 is different.

This time, don't just think about the bond.

Think like the credit professional assessing the business behind it.

 

The quiz is at the bottom of this article.
Using ChatGPT as a tutor to move from bond basics to better credit questions

I understood the basic idea of a bond—or at least I thought I did.

A government or company needs money. It issues a bond. An investor lends the money and receives interest. At maturity, the investor gets the principal back.

Simple enough.

Then I started asking ChatGPT questions.

If I buy a Treasury bond and sell it three months later, who pays me the interest? Why would anyone sell a $1,000 bond for $950 when they could simply hold it to maturity? When people say investors are "selling bonds," where do those bonds go? And why do bond yields rise when prices fall?

One question led to another.

What started as an attempt to understand bonds became something else: an experiment in using AI as a personal tutor.

A Few Bond Terms Before We Start

The distinction to remember: The bond's face value and coupon are contractual. Its market price and yield can change every day.

Starting with the basics

I started with a simple assumption:

Me: Bond yields go up, bondholders make more return, but bond issuers have to pay more?

ChatGPT immediately introduced an important distinction.

If a company issued a $1,000 bond paying 4%, the bondholder continues receiving the contractual coupon. If market interest rates subsequently rise to 6%, the company doesn't suddenly start paying the existing bondholder 6%.

Instead, the market price of the existing bond changes.

Why?

Because investors can now obtain approximately 6% elsewhere. The old 4% bond becomes less attractive at its original price.

Its price therefore falls until the return available to a new buyer becomes competitive with prevailing market rates.

Bond prices and bond yields move in opposite directions.

Price goes up → yield goes down.

Price goes down → yield goes up.

I understood the words.

As I would later discover, I hadn't completely internalized them.

What happens after Treasury sells a bond?

My next questions concerned where bonds actually come from.

Suppose the U.S. Treasury needs to borrow money.

Treasury auctions securities to investors in what is called the primary market.

Simplifying the transaction:

Treasury issues bond → Investor provides cash → Treasury owes interest and principal

But what happens if the investor wants to sell the bond before it reaches maturity?

They don't normally sell it back to Treasury. Instead, they sell it to another investor in the secondary market.

Investor A → Bond → Investor B

Investor B → Cash → Investor A

Treasury receives nothing from this transaction. The identity of its creditor has simply changed.

Treasury still owes the promised interest and principal, except Investor B now owns that claim.

If the investor instead holds the bond to maturity, Treasury simply repays the face value as promised.

That led to another question.

Why would someone sell a $1,000 bond for $950?

Suppose I paid $1,000 for a Treasury bond.

Interest rates subsequently rise and the bond is now worth only $950 in the secondary market.

Why would I sell?

Why not simply hold it until maturity and collect the $1,000 Treasury promised me?

There can be many reasons.

An investor might need cash. A fund might face withdrawals. A bank might need liquidity. An investment manager might want to shorten the portfolio's duration. Or another investment might now provide a better expected return.

There was also an important lesson here.

Not selling doesn't mean the loss doesn't exist.

If an asset I purchased for $1,000 can now be sold for only $950, its market value has declined by $50. I haven't realized the loss, but economically the asset is nevertheless worth less today.

If I hold the Treasury to maturity, however, and Treasury makes all the promised payments, I will still receive the contractual principal.

The $950 is therefore not Treasury changing its promise.

It is the market changing the price it is willing to pay for that promise today.

That distinction turned out to be fundamental.

Who gets the interest if I sell halfway through?

Then I pushed the example further.

Suppose I own a Treasury paying 5% and sell it three months into a six-month coupon period.

Do I receive anything for having held the bond for those three months?

Economically, yes.

Interest accumulates while the bond is held. This is accrued interest.

If the six-month coupon is $25 and I sell halfway through the period, approximately $12.50 has accrued during my ownership.

Treasury doesn't need to divide the next coupon between the two investors.

Instead, the bond-market settlement process accounts for the accrued interest. The new owner eventually receives the full coupon from Treasury, while the previous owner is compensated for the portion attributable to their period of ownership.

That prompted another question: What if I desperately need money and am prepared to take a haircut?

Again, the market provides the answer.

I can accept a lower price for my bond, but I still need someone willing to buy it.

Which brought me back to supply and demand.

What does "nobody is buying U.S. bonds" actually mean?

I had heard variations of this expression many times.

But taken literally, it doesn't make much sense.

If a bond is sold in the secondary market, someone bought it.

There aren't enough investors willing to buy at the current price and yield.

Suppose sellers want $1,000. Buyers aren't interested. The price falls to $980. Still insufficient demand. $960. More buyers appear. $940. Now perhaps enough investors consider the return attractive. The market clears.

Selling pressure → price falls → yield rises → additional buyers become interested

This also explains why a Treasury-market selloff can eventually affect the U.S. government's borrowing costs.

If existing 10-year Treasuries are trading at yields around 5%, Treasury cannot realistically expect investors to enthusiastically buy a comparable new issue yielding only 3%.

New Treasury debt has to compete with securities already available in the secondary market.

Then I asked ChatGPT to stop teaching me

At this point, I thought I understood it.

So I changed the exercise.

Instead of asking ChatGPT another question, I said:

"Test my understanding starting with the basics. Five questions to get started."

This changed the role of AI.

ChatGPT stopped being an answer engine and became an examiner.

It asked what would happen to the price of my existing 4% bond if newly issued comparable Treasuries yielded 6%.

I answered correctly:

"The price will go down because there won't be as much demand for a 4% yield when new bonds are paying 6%."

Good.

Then the questions became slightly harder.

ChatGPT asked:

You own a $1,000 Treasury paying a 5% coupon. Market yields suddenly fall to 3%. What should happen to the market price of your bond?

My answer?

"Market price would fall."

Wrong.

I had just demonstrated that I understood the explanation but hadn't fully internalized the relationship.

If my bond pays 5% while comparable new bonds offer only 3%, my bond has become more attractive.

Investors should therefore be willing to pay more for it.

Demand rises → price rises → yield falls toward the prevailing market return.

Then ChatGPT asked what would normally happen if a recession caused investors to rush into Treasuries as a safe haven.

I made essentially the same mistake again.

I said prices would fall and yields would rise.

But if investors are rushing into Treasuries:

Demand rises → prices rise → yields fall.

ChatGPT had identified the weakness in my mental model.

I understood why investors behaved as they did. What I hadn't yet mastered was translating that behaviour into the correct price-and-yield movement.

The tutoring session had found something that simply continuing to read explanations might not have exposed.

The rule I needed to remember

PRICE ↑ = YIELD ↓

PRICE ↓ = YIELD ↑

Or perhaps an even more intuitive version:

When everyone wants to lend you money, you don't have to pay them as much.

When people become reluctant to lend to you, you have to pay them more.

That applies not only to governments.

It brings us directly back to business credit.

What does any of this have to do with assessing a customer?

Quite a lot—particularly when assessing publicly traded companies with bonds trading in capital markets.

A corporate bond isn't simply an investment security.

Its market price and yield can also provide information about how investors perceive the company's future credit risk.

Consider a hypothetical customer.

Six months ago:

10-year government yield: 4.0%

Customer's bond yield: 5.5%

The difference—or credit spread—is 1.5 percentage points, or 150 basis points.

Now suppose six months later:

10-year government yield: 4.1%

Customer's bond yield: 8.5%

The government yield barely changed.

But investors now require 8.5% to hold the customer's debt.

The credit spread has widened from approximately 150 to 440 basis points.

Something has changed.

That doesn't automatically mean the customer is going bankrupt.

It does mean I should probably start asking questions.

The market may be telling us something

A conventional commercial credit review might tell me:

·        The customer is paying within terms.

·        Its latest financial statements appear acceptable.

·        There have been no returned payments.

·        The credit rating hasn't changed.

·        Management isn't reporting a liquidity problem.

Yet thousands of investors collectively buying and selling the company's debt are suddenly demanding substantially greater compensation for taking its credit risk.

That deserves attention.

Perhaps investors are concerned about upcoming debt maturities. Perhaps free cash flow is deteriorating. Perhaps leverage is increasing. Perhaps earnings guidance has weakened. Perhaps an acquisition changed the company's capital structure. Perhaps refinancing that previously looked straightforward is becoming much more expensive.

Or perhaps the market is simply wrong.

The bond yield doesn't give me the answer.

It gives me a signal worth investigating.

That is an important distinction.

From backward-looking credit to forward-looking signals

Commercial credit analysis naturally relies heavily on historical information.

Financial statements tell us what happened during a previous reporting period. Payment history tells us how the customer behaved yesterday. A credit report largely aggregates information that already exists.

But our real question is forward-looking:

Will this business be able and willing to pay us six months from now?

For companies with publicly traded debt, bond markets provide another piece of evidence.

Not an oracle. Not a replacement for financial analysis. And certainly not an automatic credit decision.

But potentially an early-warning signal.

If the market suddenly demands substantially more compensation for lending to one of my customers while government yields and comparable-company yields remain relatively stable, I want to understand why.

What does the bond market think is changing that isn't yet visible in my traditional credit information?

That is a very different way of looking at a bond yield.

What I learned about using AI

I started this exercise trying to understand bonds.

But I also learned something about using AI.

There is a significant difference between asking AI:

"Explain bond yields to me."

and saying:

"Test whether I actually understand bond yields."

The first gives me information.

The second forces me to retrieve the information, apply it and expose weaknesses in my reasoning.

And when I answered incorrectly, ChatGPT didn't simply give me another generic explanation. It could see the particular relationship I was getting wrong and concentrate the lesson there.

That suggests a simple way of using AI for professional development:

Ask → challenge → apply → get tested → make mistakes → correct the mental model → apply it to your work.

The objective isn't to have AI do the thinking for us.

It's to use AI to make us do more of the thinking ourselves.

And in this case, I started by asking ChatGPT how bonds work.

I ended up asking a much more useful question about credit:

What might the capital markets be telling me about a customer's ability to pay tomorrow that I can't yet see from how they're paying me today?

First ask: Which cash flow is negative?

Large-company examples establish the principle

Recent public-company results show that negative cash flow can arise for very different reasons:

Amazon generated approximately US$161.4 billion in operating cash flow for the 12 months ended June 2026, yet reported negative free cash flow of US$7.6 billion after exceptionally heavy property and equipment spending, largely for artificial-intelligence infrastructure.

Moderna used US$630 million in operating cash during the first quarter of 2026 while continuing to invest in research, development and its product pipeline. Here, the cash consumption occurs within operations and carries the risk that future products may not succeed.

Uber reported US$2.862 billion of operating cash flow and US$2.792 billion of free cash flow in the second quarter of 2026. After years of spending to build its platform and customer base, it illustrates the outcome investors hope a growth strategy will eventually produce.

These examples are instructive, but most businesses do not operate with billion-dollar cash reserves. What does the issue look like for a company with roughly $10 million in annual revenue?

Three $10-million-company examples

The following cases are hypothetical, simplified examples designed to show how the same cash-flow principles appear in a privately held business.

1. A distributor building inventory

 

The distributor purchased inventory for a new product line or seasonal sales period. Negative operating cash flow may be reasonable if the inventory is supported by demand, sells quickly and produces an adequate margin.

Credit concern: Inventory can become obsolete, sell more slowly than forecast or require price reductions. The lender or supplier should examine inventory turnover, purchase commitments, customer orders and available borrowing capacity.

2. A manufacturer adding capacity

 

The existing business generates cash, but the equipment purchase creates temporary negative free cash flow. The investment may be sound if it increases output, reduces unit costs or enables new contracts.

Credit concern: The company must fund both the equipment and the working capital required by higher production. Capacity does not automatically create demand, and growth can increase receivables and inventory before producing cash.

3. A profitable service company running short of cash

 

The income statement reports a profit, but customers are not paying quickly enough. Unlike the first two examples, the cash shortfall may not represent a deliberate investment. It may reflect weak credit decisions, billing disputes, poor receivables management or deteriorating customer quality.

Credit concern: Sales growth can conceal the problem. Review aging trends, customer concentration, disputes, bad-debt experience and the difference between reported revenue and cash received.

The questions management—and creditors—should ask

·        What exactly is consuming the cash?

·        Is the outflow planned, measurable and time-limited?

·        What return is expected, and when should it appear?

·        How much liquidity remains if the plan takes longer than expected?

·        Is the company funding productive growth—or covering weaknesses in its existing operations?

What this exchange demonstrates about learning with AI

An AI tutor is most useful when the user remains active. Instead of treating the first response as final, ask for examples, challenge assumptions, request another level of explanation and connect the answer to a situation you actually understand.

·        Ask the model to explain the concept in plain language—and then in professional terms.

·        Request examples, counterexamples and a comparison between healthy and unhealthy cases.

·        Ask to see the concept inside a calculation, document or financial statement.

·        Scale the example to your industry, organization or level of responsibility.

·        Ask what assumptions, risks or exceptions may have been overlooked.

The caveat is important: An LLM can produce a confident answer that is incomplete, outdated or wrong. It does not know your full circumstances, and it cannot replace accountable professional judgment. Verify material facts against primary sources, protect confidential information and consult qualified professionals when the consequences matter.

Sources and credits

1. Amazon.com, Inc., Second Quarter 2026 Results, July 30, 2026. View source

2. Moderna, Inc., Quarterly Report for the period ended March 31, 2026 (Form 10-Q). View source

3. Uber Technologies, Inc., Second Quarter 2026 Results, August 5, 2026. View source

Disclaimer

This article was developed through an iterative conversation with generative artificial intelligence and reviewed for publication. AI-generated explanations may contain errors, omissions, outdated information or invented details. This material is provided for general educational and discussion purposes only and does not constitute accounting, financial, investment, legal, tax or credit advice. It should not replace independent verification, professional judgment or advice tailored to a specific organization. Public-company figures are drawn from the cited disclosures and may include non-GAAP measures such as free cash flow. The $10-million-company examples are hypothetical and do not represent actual companies. Do not submit confidential, personal or proprietary information to a public AI service without appropriate authorization and safeguards.
PS: Want to refresh or advance your financial analysis skill, check out our CCP and ECCP courses

Collections Trend Snapshot:
Our latest survey reveals a mixed but cautiously strained picture in Canadian accounts receivable. While 43% of respondents reported collection periods remaining the same, a notable 33% saw them increase (slower collections), outpacing the 22% who experienced faster recoveries. This net tilt toward extended payment times aligns with Canada’s subdued economic growth, persistent high household and business debt levels, and sector-specific pressures amid trade uncertainties and elevated interest rates.

What are you seeing in the trenches? We’d love to hear your firsthand insights on customer payment behavior, credit risk signals, or strategies that are working (or not) right now. Drop your observations in the comments or reply directly; your perspective helps paint a clearer picture for the community.

In this week's I ASKED AI blog, I asked AI to create an AI Readiness Assessment. 

Give it a try here! Just remember, AI can be a little biased!

What shaped the business landscape as we enter 2026

As we kick off a new year, it’s worth reflecting on the business developments that shaped decision-making, risk, investment, and strategy throughout 2025. Here are the 10 business stories that dominated headlines last year:

1. AI Became Core to Business Strategy
   Artificial intelligence moved beyond experimentation. In 2025, AI became embedded in operations, finance, marketing, customer service, and decision-making — reshaping productivity expectations and competitive advantage.
2. China’s BYD Overtook Tesla as the World’s Largest EV Maker
   The shift marked a major realignment in global manufacturing power, highlighting China’s growing dominance in electric vehicles and supply chains.
3. A Record Year for Mega-Mergers and Acquisitions
   2025 saw one of the strongest M&A environments in decades, with a surge in $10B+ deals driven by consolidation, scale, and AI-related synergies.
4. AI Drove Massive Wealth Creation — and Valuation Debates
   AI-linked stocks added hundreds of billions in market value, reigniting conversations around asset bubbles, sustainability of growth, and long-term fundamentals.
5. Regulatory Enforcement Softened in Key Markets
   Fines related to financial crimes and compliance violations dropped sharply in the U.S., signaling a shift in enforcement priorities and regulatory posture.
6. Corporate Leadership Structures Were Rebuilt Around AI
   Major firms reorganized leadership teams, roles, and reporting lines to prioritize AI integration and commercialization.
7. Markets Grappled With Volatility and Valuation Reality Checks
   Strong earnings expectations collided with economic uncertainty, creating sharp market swings — particularly in tech and growth sectors.
8. Trade Policy and Tariff Uncertainty Returned to the Spotlight
   Shifting geopolitical alliances and trade policies once again influenced supply chains, pricing strategies, and global risk planning.
9. Global Growth Slowed — But Avoided a Hard Landing
   Economic growth moderated worldwide, forcing businesses to focus on efficiency, cash flow management, and credit discipline rather than expansion alone.
10. Consolidation Accelerated Across Multiple Industries
    Beyond tech, industries such as logistics, media, energy, and financial services saw increased consolidation to manage costs, scale operations, and reduce risk.

As of November 2025, the global economic outlook remains one of moderate growth with considerable headwinds. In many developed economies (e.g., the U.S., Japan, the eurozone) growth is positive but modest; business confidence remains cautiously optimistic amid elevated uncertainty. In the UK growth is weak and risks remain elevated, although I did not find clear data of declining employment across the board.

Global trade tensions — particularly between the U.S. and China — and geopolitical risks continue to weigh on investment and trade flows. In China, growth prospects have improved somewhat, with official forecasts nudged upward, though structural and policy risks persist.

Inflation remains a concern in various regions, and central banks continue to navigate the trade-off between inflation control and growth support.

In Canada, recent data show the economy grew about +0.5 % annualized in Q3, after a trade-driven contraction in Q2. While output in manufacturing and trade-exposed industries remains under pressure, household consumption has held up reasonably well and inflation has eased to about 2.2% in October.

The Bank of Canada’s latest projections point to annual growth near 1.4% in 2026-27, and while rates remain elevated. Business investment remains cautious, and down-side risks (labour market softness, housing pressures, trade uncertainty) remain relevant.

Overall, the global and Canadian economies are navigating a fragile mix of modest growth, persistent uncertainty (trade, geopolitical, policy) and inflationary pressures — as we approach the end of 2025.

I Asked AI…Which industries around the world are showing signs of economic downturn?

Many industries worldwide are displaying clear signs of economic downturn as of 2025, with several sectors particularly vulnerable due to factors like global trade tensions, high tariffs, and weakening demand.

 

Most Affected Industries

 

Manufacturing and Industrial Sectors

The global manufacturing sector is widely expected to enter a recession in 2025. This is driven by tariff wars, rising input costs, and disruptions to cross-border supply chains, especially impacting sectors relying heavily on imported inputs (e.g., automotive, machinery, electronics outside of semiconductors). Major economies such as the U.S., Europe, and Canada are seeing contractions in manufacturing output, particularly in durable goods like vehicles and machinery.

 

Wholesale and Supply Chain Businesses

Supply chain challenges have intensified since early 2025, most notably affecting wholesale and cross-border trade sectors. In Canada, both wholesale and manufacturing are facing contractions tied to U.S.-Canada border delays, increased tariffs, and diminished private investment.

 

Retail and Big-Ticket Consumer Goods

Sales in retail, especially for big-ticket items such as new vehicles, appliances, and furniture, are down as both business and consumer sentiment decline. Higher prices due to tariffs and uncertainty over incomes have led to postponed or canceled purchases.

 

Agriculture and Food Processing

Agriculture, forestry, and food manufacturing are hit hard by tariff changes and reduced cross-border demand, as retaliatory tariffs and supply disruptions persist globally. This contraction is mirrored in upstream (input providers) and downstream (exporters) segments.

 

Aerospace, Chemicals, and Primary Metals

Sectors like aerospace, chemicals, and primary metals (including aluminum) are especially vulnerable due to direct exposure to new tariffs, increased production costs, and changing supply relationships. This is cited as a significant risk in both North American and European markets.

 

Public Sector and Contract Services

The public sector in the U.S. is facing historically large workforce reductions and contractor cuts, removing a typical stabilizer for economic downturns and impacting associated industries such as consulting, infrastructure, and support services.

 

Key Drivers of the Downturn

·       Global tariff escalation: Widespread, frequently changing tariffs have disrupted trade, increased costs for manufacturers, and led to retaliatory moves by other countries.

·       Supply chain instability: Border delays and input shortages amplify risk for production and trade-dependent companies.

·       Reduced business investment and consumer spending: Economic uncertainty deters investments in new capacity and capital outlays, leading to further weakness across affected sectors.

 

Resilient Sectors

Some industries—such as healthcare, consumer staples, utilities, and essential services (e.g., auto repair, home maintenance)—are showing more resilience and are less likely to experience a major downturn in 2025.

In summary, manufacturing, supply chain, retail (durable goods), agriculture, aerospace, chemicals, and public sector contractors are currently among the industries facing the most pronounced economic headwinds globally.

What are you seeing through your crystal ball?

Introduction

Credit risk analysis doesn’t have to be a grim slog through numbers. Imagine assessing a business’s creditworthiness with the flair of a personality quiz, labeling your customer as a “Frosty Trailblazer” or a “Glacial Gambler”! In this week’s “I ASKED AI...” column, we’re adding a playful twist to our ongoing exploration of AI-driven B2B credit management. Using the fictional Tundraland Economic Development Fund’s (TEDF) lending framework, we’ve crafted an AI prompt that evaluates a business’s credit risk in a fun, quiz-style format while delivering actionable insights. Whether you’re a credit pro in Tundraland or elsewhere, this approach makes risk assessment both engaging and enlightening. Let’s see how AI turns data into personalities!

Go one step further and take the 5-question quiz to test your understanding.

The Prompt: B2B Credit Risk Personality Quiz

This prompt builds on our previous Template 2 for dynamic credit management, adapted to categorize a business’s credit risk as a “personality type” while providing practical recommendations. It’s tailored for TEDF’s context, supporting businesses in Tundraland with loans from $150,000 to $1,000,000 for those unable to secure traditional financing.

 

Prompt:
You are a B2B credit management expert tasked with evaluating a Tundraland-based business’s credit risk under net 60 terms, aligning with the Tundraland Economic Development Fund’s (TEDF) lending parameters (loans from $150,000 to $1,000,000 for businesses unable to secure traditional financing). Analyze the following information:

·        Business name and industry: [insert details]

·        Current credit limit: [insert amount]

·        Current payment terms: [net 60]

·        Recent payment performance with our company: [e.g., on-time, delayed]

·        Recent payment performance with other suppliers: [summary]

·        Updated financial metrics (revenue, profit margins, debt-to-equity ratio, liquidity): [insert data]

·        Recent credit bureau or trade reference updates: [insert findings]

·        Current outstanding balance: [insert amount]

·        Recent order frequency and transaction size: [insert data]

·        Changes in business operations: [describe]

·        Industry-specific risks or economic trends: [describe]

·        Legal or regulatory issues: [describe]

·        TEDF lending parameters: Loans from $150,000 to $1,000,000; supports Tundraland-based businesses, including Indigenous-owned, with no sector restrictions.

 

Based on this, provide:

1.     A “Credit Risk Personality” for the business, choosing one of:

o   Frosty Trailblazer: Reliable, low-risk, consistent payments, and robust financials.

o   Icy Voyager: Moderate risk, some delays or financial strain but manageable.

o   Glacial Gambler: High risk, frequent delays, or significant financial/operational issues.

o   Polar Wildcard: Unpredictable due to mixed signals (e.g., strong contract but weak financials).

2.     A brief, fun description of the personality type (1–2 sentences).

3.     Key factors driving the personality assignment (financial, operational, industry-specific).

4.     A recommendation to maintain, increase, or decrease the credit limit (with justification, considering TEDF’s parameters).

5.     Suggested payment terms or conditions to manage risk.

6.     A monitoring plan with key metrics and review frequency.

 

Sample Quiz: Snowdrift Supplies

Let’s test the prompt with synthetic data for a fictional Tundraland-based retail business, Snowdrift Supplies, which operates a general store serving remote Arctic communities.

 

Synthetic Data:

·        Business name and industry: Snowdrift Supplies, retail (general store in Tundraland)

·        Current credit limit: $100,000

·        Current payment terms: Net 60

·        Recent payment performance with our company: 80% on-time, 15% delayed by 10–15 days, 5% delayed by 30+ days

·        Recent payment performance with other suppliers: Mixed; two suppliers report consistent on-time payments, one reports frequent 20-day delays

·        Updated financial metrics:

o   Revenue: $5M (down 10% year-over-year)

o   Profit margin: 8% (down from 12% last year)

o   Debt-to-equity ratio: 1.5 (up from 1.2)

o   Current ratio (liquidity): 1.1 (down from 1.4)

·        Recent credit bureau or trade reference updates: Arctic Credit Bureau score of 70 (indicating moderate payment delays)

·        Current outstanding balance: $65,000

·        Recent order frequency and transaction size: Orders decreased from monthly to bi-monthly; average transaction size dropped from $25,000 to $15,000

·        Changes in business operations: Layoffs of 10% of workforce; new contract with a community cooperative for bulk supply orders

·        Industry-specific risks or economic trends: High shipping costs to remote communities (up 15%); declining consumer spending due to economic slowdown; steady demand for essential goods in Tundraland

·        Legal or regulatory issues: No known issues

·        TEDF lending parameters: Loans from $150,000 to $1,000,000; supports Tundraland-based businesses unable to secure traditional financing

 

 

 

AI-Generated Output:

 

1.     Credit Risk Personality

 

Icy Voyager

 

2. Personality Description

Snowdrift Supplies cruises through Tundraland’s icy markets with steady sales of essentials, but a few financial icebergs and delayed payments keep it from smooth sailing.

 

3. Key Factors Driving Personality Assignment

·        Financial strain: Revenue down 10%, profit margins at 8% (from 12%), and a current ratio of 1.1 signal tight liquidity, common for retail in remote Arctic regions.

·        Payment delays: 20% of payments delayed (15% by 10–15 days, 5% by 30+ days) and an Arctic Credit Bureau score of 70 indicate moderate cash flow challenges.

·        Industry dynamics: High shipping costs (up 15%) and reduced consumer spending increase risk, but demand for essential goods provides a stable base.

·        New contract: A bulk supply deal with a community cooperative boosts revenue potential, aligning with TEDF’s support for community-focused businesses.

 

4. Credit Limit Recommendation

 

Recommendation: Maintain the credit limit at $100,000.

Justification: Despite financial challenges, Snowdrift Supplies benefits from steady demand for essential goods and a new cooperative contract, supporting revenue recovery. The $100,000 limit is below TEDF’s $150,000 minimum loan threshold but suitable given the $65,000 outstanding balance and smaller transaction sizes ($15,000). Maintaining the limit aligns with TEDF’s mission to foster Tundraland’s economy while managing risk.

 

5. Suggested Payment Terms or Conditions

·        Adjust to net 45: Shorten terms from net 60 to reduce DSO and align with the 45-day target, reflecting retail’s steady but seasonal cash flow.

·        Prepayment for bulk orders: Require 20% upfront payment for orders over $10,000 to secure cash flow from the cooperative contract.

·        Inventory collateral: Secure credit with inventory (e.g., non-perishable goods) to align with TEDF’s ability to take security on assets.

 

6. Monitoring Plan

·        Metrics to track:

o   DSO: Monitor monthly to ensure alignment with the 45-day target.

o   Sales trends: Track revenue from essential goods and cooperative contract to confirm stability.

o   Payment timeliness: Monitor late payment frequency, especially in winter when spending dips.

o   Shipping costs: Track increases in logistics costs impacting margins.

·        Review frequency: Every 90 days, or upon receipt of updated financials or cooperative contract performance data.

 

How to Use This Prompt

·        Customize it: Plug in your customer’s data and tweak personality names to fit your brand (e.g., “Snowy Stalwart” instead of “Frosty Trailblazer”).

·        Add Arctic flair: Incorporate Tundraland-specific risks (e.g., winter shipping delays) for extra relevance.

·        Integrate with TEDF: Use TEDF’s application process (financials, business plans) to validate data and ensure alignment with lending criteria.

·        Share the fun: Present quiz results to your team to spark lively discussions about credit decisions.

 

Pro Tip

Run this prompt with other Tundraland industries (e.g., eco-tourism, fisheries) to see how AI crafts unique personalities for each sector’s risks. For deeper insights, weight key metrics (e.g., payment delays over revenue) based on your credit policy.

To demonstrate how Template 2 from the "Advanced AI Prompt for Dynamic B2B Credit Management" yields a different assessment when applied to a different industry, we’ll use the same synthetic data but change the industry from industrial equipment manufacturing to software-as-a-service (SaaS). The SaaS industry typically has different risk profiles, such as recurring revenue streams, lower physical asset dependency, and sensitivity to customer churn or market competition. This shift should lead to a notably different evaluation due to the industry’s unique financial and operational dynamics.

Synthetic Data for Simulation (Updated Industry)

Business Information:

• Business name and industry: Apex Solutions, software-as-a-service (SaaS)
• Current credit limit: $100,000
• Current payment terms: Net 60
• Recent payment performance with our company: 80% on-time, 15% delayed by 10–15 days, 5% delayed by 30+ days
• Recent payment performance with other suppliers: Mixed; two suppliers report consistent on-time payments, one reports frequent 20-day delays
• Updated financial metrics:
• Revenue: $5M (down 10% year-over-year)
• Profit margin: 8% (down from 12% last year)
• Debt-to-equity ratio: 1.5 (up from 1.2)
• Current ratio (liquidity): 1.1 (down from 1.4)
• Recent credit bureau or trade reference updates: Dun & Bradstreet PAYDEX score of 70 (indicating moderate payment delays)
• Current outstanding balance with our company: $65,000
• Recent order frequency and transaction size: Orders decreased from monthly to bi-monthly; average transaction size dropped from $25,000 to $15,000
• Changes in business operations: Recent layoffs of 10% of workforce; new contract secured with a major client
• Industry-specific risks or economic trends: High competition in SaaS market; customer churn rates increasing by 5% industry-wide; venture capital funding for SaaS startups declining
• Legal or regulatory issues: No known issues
• Our company’s credit policy and risk appetite: Maximum exposure of $150,000 per customer; target DSO (Days Sales Outstanding) of 45 days

Applying Template 2: Dynamic B2B Credit Limit Adjustment and Risk Monitoring

Using the synthetic data with the SaaS industry context, here’s how the template is applied, followed by the AI-generated output.

Dynamic Credit Review for Apex Solutions

You are a B2B credit management expert tasked with reviewing and adjusting an existing credit limit for a business customer operating under net 60 terms. The goal is to ensure the credit limit remains aligned with the customer’s current financial health, market conditions, and our company’s risk tolerance. Analyze the following updated information:

• Business name and industry: Apex Solutions, software-as-a-service (SaaS)
• Current credit limit: $100,000
• Current payment terms: Net 60
• Recent payment performance with our company: 80% on-time, 15% delayed by 10–15 days, 5% delayed by 30+ days
• Recent payment performance with other suppliers: Mixed; two suppliers report consistent on-time payments, one reports frequent 20-day delays
• Updated financial metrics:
• Revenue: $5M (down 10% year-over-year)
• Profit margin: 8% (down from 12% last year)
• Debt-to-equity ratio: 1.5 (up from 1.2)
• Current ratio (liquidity): 1.1 (down from 1.4)
• Recent credit bureau or trade reference updates: Dun & Bradstreet PAYDEX score of 70 (indicating moderate payment delays)
• Current outstanding balance with our company: $65,000
• Recent order frequency and transaction size: Orders decreased from monthly to bi-monthly; average transaction size dropped from $25,000 to $15,000
• Changes in business operations: Recent layoffs of 10% of workforce; new contract secured with a major client
• Industry-specific risks or economic trends: High competition in SaaS market; customer churn rates increasing by 5% industry-wide; venture capital funding for SaaS startups declining
• Legal or regulatory issues: No known issues
• Our company’s credit policy and risk appetite: Maximum exposure of $150,000 per customer; target DSO of 45 days

Based on this information, provide:

1. An updated assessment of the customer’s creditworthiness under the current payment terms.
2. Key changes in risk factors or strengths since the last credit review.
3. A recommendation to maintain, increase, or decrease the credit limit (with justification, including specific financial or operational triggers).
4. Suggested adjustments to payment terms or conditions (e.g., shorter terms, partial prepayments, or collateral requirements).
5. A monitoring plan, including specific metrics to track (e.g., DSO, late payment frequency) and recommended review frequency.
6. Any red flags or early warning signals to watch for in the next 3–6 months.

AI-Generated Output

1. Updated Assessment of Creditworthiness

Apex Solutions’ creditworthiness has moderately declined but remains viable for limited credit under net 60 terms. The 10% revenue drop and reduced profit margin (8% vs. 12%) reflect challenges in the competitive SaaS market, while the current ratio of 1.1 and increased debt-to-equity ratio (1.5 vs. 1.2) indicate constrained liquidity and higher leverage. Payment performance is mixed, with 20% of payments to our company delayed and a PAYDEX score of 70, suggesting moderate risk. However, the SaaS industry’s recurring revenue model and the new major client contract provide a buffer, as subscription-based cash flows are more predictable than in asset-heavy industries.

2. Key Changes in Risk Factors and Strengths

Risk Factors:

• Revenue and profitability decline: A 10% revenue drop and lower margins (8% vs. 12%) signal potential customer churn or pricing pressure in the SaaS market.
• Liquidity constraints: A current ratio of 1.1 (down from 1.4) suggests limited ability to cover short-term obligations.
• Payment delays: 20% of payments to our company are late (15% by 10–15 days, 5% by 30+ days), with one supplier reporting frequent 20-day delays.
• Industry pressures: Rising churn rates (5% industry-wide) and declining venture capital funding increase financial and operational risks for SaaS firms.

Strengths:

• Recurring revenue model: SaaS businesses benefit from subscription-based cash flows, which provide more stability than one-off sales in other industries.
• New client contract: A major client contract could boost recurring revenue, potentially offsetting the 10% revenue decline if the contract is long-term.
• Operational agility: Layoffs of 10% of the workforce suggest cost-cutting measures to improve margins, and SaaS firms typically have lower fixed asset burdens.

3. Credit Limit Recommendation

Recommendation: Maintain the credit limit at $100,000. Justification: Despite financial declines, the SaaS industry’s recurring revenue model mitigates some risks compared to asset-heavy sectors. The new client contract could stabilize or increase revenue, supporting Apex’s ability to service the current credit limit. The outstanding balance of $65,000 is manageable within the $100,000 limit, and reduced order sizes ($15,000 vs. $25,000) suggest lower credit utilization. Decreasing the limit could strain the business relationship, while an increase is unwarranted given liquidity constraints and payment delays. This aligns with the company’s $150,000 exposure cap.

4. Suggested Adjustments to Payment Terms or Conditions

• Shorten payment terms: Adjust from net 60 to net 30 to reduce DSO and align with the target of 45 days, leveraging the predictability of SaaS cash flows.
• Require payment milestones: For orders exceeding $10,000, require 25% payment upon contract signing and 25% at service delivery to reduce exposure.
• Contract verification: Request details on the new client contract (e.g., term length, revenue impact) as a condition for maintaining the credit limit.

5. Monitoring Plan

• Metrics to track:
• DSO: Monitor monthly to ensure alignment with the 45-day target.
• Customer churn rate: Track Apex’s churn rate to assess revenue stability, as SaaS firms are highly sensitive to customer retention.
• Recurring revenue (MRR/ARR): Monitor monthly/annual recurring revenue to gauge the impact of the new client contract.
• Payment timeliness: Track late payment frequency and severity monthly.
• Review frequency: Conduct reviews every 60 days, given the fast-paced SaaS market, or upon receipt of updated financials or contract details.

6. Red Flags to Watch

• Rising churn rates: Customer churn exceeding 7% could further erode revenue.
• Worsening liquidity: Current ratio falling below 1.0 or delays in subscription payments.
• Funding issues: Failure to secure additional funding in a tightening VC market, impacting cash reserves.
• Contract risks: Delays or cancellation of the new client contract, undermining expected revenue growth.

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How This Differs from the Previous Assessment

The evaluation for Apex Solutions (SaaS) differs significantly from the prior assessment for Apex Manufacturing (industrial equipment) due to industry-specific dynamics:

• Credit Limit Decision: The SaaS assessment recommends maintaining the $100,000 credit limit, while the manufacturing assessment recommended decreasing it to $75,000. The SaaS industry’s recurring revenue model and lower asset intensity provide more cash flow predictability, reducing the need for a limit reduction despite similar financial declines.
• Risk Perception: The SaaS evaluation emphasizes customer churn and funding risks, which are critical in the tech sector, whereas the manufacturing assessment focused on supply chain disruptions and demand softness. The SaaS model’s subscription-based revenue is a stabilizing factor, unlike manufacturing’s reliance on physical production.
• Payment Terms: The SaaS assessment suggests tighter net 30 terms with milestone payments to leverage predictable cash flows, while the manufacturing assessment used net 45 terms with prepayments to address higher operational risks.
• Monitoring Focus: The SaaS monitoring plan prioritizes churn rates and recurring revenue metrics (MRR/ARR), unique to subscription businesses, while the manufacturing plan focused on order patterns and financial ratios tied to physical operations.

Why the Industry Matters

The SaaS industry’s recurring revenue and lower fixed costs create a more resilient financial profile, even with similar declines in revenue and liquidity. This leads to a less conservative credit decision compared to the manufacturing sector, where physical supply chain risks and demand volatility amplify concerns. The new client contract has a stronger positive impact in SaaS, as it directly boosts predictable subscription revenue.

Using This in Practice

1. Replace synthetic data with real SaaS customer data, including MRR, churn rates, or funding details.
2. Customize the prompt to weight SaaS-specific metrics (e.g., churn, ARR) based on your credit policy.
3. Validate the AI’s output with industry benchmarks and internal risk thresholds.
4. Integrate with CRM tools to track churn or payment patterns in real time.

Credit Review for Apex Manufacturing

You are a B2B credit management expert tasked with reviewing and adjusting an existing credit limit for a business customer operating under net 60 terms. The goal is to ensure the credit limit remains aligned with the customer’s current financial health, market conditions, and our company’s risk tolerance. Analyze the following updated information:

• Business name and industry: Apex Manufacturing, industrial equipment manufacturing
• Current credit limit: $100,000
• Current payment terms: Net 60
• Recent payment performance with our company: 80% on-time, 15% delayed by 10–15 days, 5% delayed by 30+ days
• Recent payment performance with other suppliers: Mixed; two suppliers report consistent on-time payments, one reports frequent 20-day delays
• Updated financial metrics:
• Revenue: $5M (down 10% year-over-year)
• Profit margin: 8% (down from 12% last year)
• Debt-to-equity ratio: 1.5 (up from 1.2)
• Current ratio (liquidity): 1.1 (down from 1.4)
• Recent credit bureau or trade reference updates: Dun & Bradstreet PAYDEX score of 70 (indicating moderate payment delays)
• Current outstanding balance with our company: $65,000
• Recent order frequency and transaction size: Orders decreased from monthly to bi-monthly; average transaction size dropped from $25,000 to $15,000
• Changes in business operations: Recent layoffs of 10% of workforce; new contract secured with a major client
• Industry-specific risks or economic trends: Supply chain disruptions increasing raw material costs by 15%; demand for industrial equipment softening due to economic slowdown
• Legal or regulatory issues: No known issues
• Our company’s credit policy and risk appetite: Maximum exposure of $150,000 per customer; target DSO of 45 days

Based on this information, provide:

1. An updated assessment of the customer’s creditworthiness under the current payment terms.
2. Key changes in risk factors or strengths since the last credit review.
3. A recommendation to maintain, increase, or decrease the credit limit (with justification, including specific financial or operational triggers).
4. Suggested adjustments to payment terms or conditions (e.g., shorter terms, partial prepayments, or collateral requirements).
5. A monitoring plan, including specific metrics to track (e.g., DSO, late payment frequency) and recommended review frequency.
6. Any red flags or early warning signals to watch for in the next 3–6 months.

AI-Generated Output

1. Updated Assessment of Creditworthiness

Apex Manufacturing’s creditworthiness has weakened since the last review. The company’s financial metrics show a 10% revenue decline, a reduced profit margin (8% vs. 12%), and a lower current ratio (1.1 vs. 1.4), indicating tighter liquidity. The increased debt-to-equity ratio (1.5 vs. 1.2) suggests higher financial leverage, which elevates risk. Payment performance is concerning, with 20% of payments to our company delayed (15% by 10–15 days, 5% by 30+ days) and a PAYDEX score of 70 reflecting moderate delays with other suppliers. However, a new major client contract signals potential for revenue recovery, though its impact is not yet reflected in financials.

2. Key Changes in Risk Factors and Strengths

Risk Factors:

• Declining financial health: Reduced revenue, profit margins, and liquidity, coupled with higher debt levels, indicate financial strain.
• Payment delays: 20% of payments to our company are late, and one supplier reports frequent 20-day delays.
• Industry headwinds: Supply chain disruptions and softening demand increase operational and financial risks.
• Reduced order frequency: Bi-monthly orders (down from monthly) and smaller transaction sizes ($15,000 vs. $25,000) suggest lower business activity.

Strengths:

• New client contract: A major contract could improve future revenue and cash flow, though its scale and timeline are unclear.
• Established business: Apex’s long-standing presence in the industry provides some stability despite current challenges.

3. Credit Limit Recommendation

Recommendation: Decrease the credit limit from $100,000 to $75,000.
Justification: The combination of declining financial metrics, payment delays, and industry risks warrants a cautious approach to limit exposure. The current outstanding balance of $65,000 is close to the proposed limit, and reduced order frequency suggests lower credit needs. The new client contract is a positive signal but lacks immediate financial impact to justify maintaining or increasing the limit. This adjustment stays within the company’s $150,000 exposure cap while balancing risk and relationship maintenance.

4. Suggested Adjustments to Payment Terms or Conditions

• Shorten payment terms: Move from net 60 to net 45 to accelerate cash inflows and reduce DSO, aligning closer to the company’s target of 45 days.
• Partial prepayment: Require 20% upfront payment on orders exceeding $10,000 to mitigate risk on larger transactions.
• Periodic review clause: Include a clause requiring a credit review every 90 days to reassess financial health and contract impact.

5. Monitoring Plan

• Metrics to track:
• DSO: Monitor to ensure it trends toward the target of 45 days.
• Payment timeliness: Track the percentage of on-time vs. delayed payments monthly.
• Financial updates: Request quarterly financial statements to monitor revenue, liquidity, and debt levels.
• Order patterns: Track order frequency and transaction size for signs of recovery or further decline.
• Review frequency: Conduct reviews every 90 days or upon receipt of updated financials or major operational changes (e.g., new contracts or layoffs).

6. Red Flags to Watch

• Increased payment delays: Delays exceeding 30 days on more than 10% of invoices.
• Worsening liquidity: Current ratio dropping below 1.0 or significant cash flow constraints reported.
• Industry deterioration: Further supply chain cost increases (>20%) or demand drops impacting Apex’s revenue.
• Operational setbacks: Additional layoffs or loss of the new client contract.

Introduction

Last week, we shared a prompt template for evaluating and recommending B2B credit limits, focusing on initial credit assessments for short-term payment terms like net 30 or net 60. This week, we’re diving deeper with a follow-up template designed for dynamic credit management. This advanced prompt helps B2B credit professionals adjust credit limits and monitor ongoing risks based on real-time business performance, market conditions, and customer behavior. By leveraging AI to analyze evolving data, you can proactively manage credit exposure and maintain healthy cash flow while fostering strong client relationships.

 

Template 2: Dynamic B2B Credit Limit Adjustment and Risk Monitoring

You are a B2B credit management expert tasked with reviewing and adjusting an existing credit limit for a business customer operating under [insert payment terms, e.g., net 30, net 60]. The goal is to ensure the credit limit remains aligned with the customer’s current financial health, market conditions, and our company’s risk tolerance. Analyze the following updated information:

 

-            Business name and industry: [insert details]

-            Current credit limit: [insert amount]

-            Current payment terms: [net 30, net 60, etc.]

-            Recent payment performance with our company: [e.g., on-time, delayed by X days, missed payments]

-            Recent payment performance with other suppliers (if available): [summary]

-            Updated financial metrics (revenue trends, profit margins, debt-to-equity ratio, liquidity ratios): [insert data]

-            Recent credit bureau or trade reference updates: [insert findings]

-            Current outstanding balance with our company: [insert amount]

-            Recent order frequency and transaction size: [insert data]

-            Changes in business operations (e.g., expansion, downsizing, new management): [describe]

-            Industry-specific risks or economic trends (e.g., supply chain disruptions, inflation): [describe]

-            Any legal or regulatory issues affecting the customer or industry: [describe]

-            Our company’s credit policy and risk appetite: [e.g., maximum exposure, preferred DSO, etc.]

 

Based on this information, provide:

1.        An updated assessment of the customer’s creditworthiness under the current payment terms.

2.        Key changes in risk factors or strengths since the last credit review.

3.        A recommendation to maintain, increase, or decrease the credit limit (with justification, including specific financial or operational triggers).

4.        Suggested adjustments to payment terms or conditions (e.g., shorter terms, partial prepayments, or collateral requirements).

5.        A monitoring plan, including specific metrics to track (e.g., DSO, late payment frequency) and recommended review frequency.

6.        Any red flags or early warning signals to watch for in the next 3–6 months.

 How to Use This Template

-            Tailor the Prompt**: Adjust the fields to reflect your company’s specific credit policies, risk thresholds, and data availability. Include only the metrics or details your team routinely collects.

-            Focus on Changes**: Emphasize recent changes in the customer’s financials, operations, or market environment to ensure the AI prioritizes dynamic factors over static ones.

-            Incorporate Real-Time Data**: If possible, integrate real-time data from your CRM, ERP, or credit bureau APIs to keep the analysis current.

-            Validate AI Output**: Use the AI’s recommendations as a starting point, but always cross-check with your team’s expertise and industry knowledge before implementing changes.

-            Iterate Regularly**: Schedule periodic reviews (e.g., quarterly) using this prompt to keep credit limits aligned with evolving risks and opportunities.

 

Why This Matters

In B2B credit management, static credit limits can become outdated as customer financials, market conditions, or payment behaviors shift. This advanced prompt empowers credit teams to use AI for proactive adjustments, reducing the risk of overexposure while supporting sales growth. By incorporating ongoing monitoring, you can catch early warning signs—such as delayed payments or declining liquidity—before they escalate into bad debt.

Pro Tip

To enhance this prompt, consider adding specific weightings to risk factors based on your industry. For example, if late payments are a stronger predictor of default in your sector, instruct the AI to prioritize payment performance over other metrics. You can also integrate this prompt with automated workflows in your credit management software for seamless, data-driven decision-making.

Next Week - We will test this template with synthetic data.

Introduction

In B2B transactions, extending credit means supplying goods or services to another business with the agreement that payment will be made within a set period—often net 30, net 60, or net 90 days. Setting the right credit limit is crucial: too high, and you risk non-payment or cash flow issues; too low, and you might limit sales or strain the business relationship. This week’s template is designed specifically for B2B credit professionals evaluating and recommending credit limits for business customers under these short-term payment terms.

Template 1: B2B Credit Limit Review and Recommendation

text

You are a B2B credit analyst evaluating a credit application from a business customer requesting to purchase goods/services on [insert payment terms, e.g., net 30, net 60]. Review the following information:

- Business name and industry: [insert details]

- Years in operation: [insert number]

- Requested credit limit: [insert amount]

- Requested payment terms: [net 30, net 60, etc.]

- Recent order volume and average transaction size: [insert data]

- Payment history with our company (if applicable): [on-time, late payments, etc.]

- Payment history with other suppliers (if available): [summary]

- Financial statements (revenue, profit margins, debt levels): [insert summary]

- Credit bureau or trade reference reports: [insert findings]

- Current outstanding balances: [insert amount]

- Any recent changes in business operations or financial position: [describe]

- Industry risk factors or economic outlook: [describe]

Based on this information, provide:

1. An assessment of the customer’s creditworthiness for the requested terms.

2. Key risk factors and strengths relevant to short-term B2B credit.

3. A recommended credit limit and payment terms (with justification).

4. Any suggested conditions, such as requiring a personal guarantee, partial upfront payment, or periodic review.

5. Monitoring or follow-up actions to help manage risk after approval.

How to Use This Template

• Customize the fields to match your company’s credit policy and the specifics of each transaction.
• Emphasize payment terms (net 30, net 60, etc.) in your prompt to ensure the AI considers the short-term nature of the credit.
• Review the AI’s output and apply your professional judgment before making a final decision.

Disclaimer

The information and AI prompt templates provided in this series are intended for educational and illustrative purposes only. They do not constitute financial, legal, or professional advice. Always use your professional judgment and consult with relevant experts before making business decisions based on AI-generated outputs. If you choose to use real data with any AI engine or tool, be sure to anonymize all sensitive or personally identifiable information before uploading to protect privacy and comply with data protection regulations.

Join the Conversation

Have you used AI prompts to assist in setting B2B credit limits? What factors do you find most important when evaluating credit for short-term payment terms? Share your experiences and insights in the comments below—your feedback helps us all improve!

About This Article

This blog post was primarily generated using AI tools, including OpenAI's ChatGPT, which assisted in drafting the content based on best practices in prompt engineering for B2B credit professionals. While AI played a key role in creating the initial draft, the article has been thoroughly reviewed and edited by the author to ensure accuracy, clarity, and relevance to our audience.
Note: If you use AI tools with real data, always anonymize sensitive information before uploading to protect privacy and comply with data protection standards.

Stay tuned for next week’s installment, where we’ll adapt the next template for evaluating credit limit increase requests in B2B transactions!

 

In today’s fast-paced B2B credit environment, artificial intelligence is not just another tool on your desktop - it’s your digital assistant, ready to help you analyze complex data, spot risks, and make informed decisions faster than ever. By treating AI as a collaborative partner (one that augments your expertise rather than replaces it), you can streamline routine tasks, gain deeper insights, and focus on the strategic aspects of credit management that require human judgment. This series of blog articles is designed to help B2B credit professionals harness AI as an assistant, offering practical prompt templates and best practices to elevate your daily workflow.

Prompt Engineering Best Practices for B2B Credit

Prompt engineering is the art of crafting precise, clear, and context-rich inputs to guide AI models toward generating accurate, relevant, and actionable outputs. In the B2B credit domain, where decisions hinge on complex financial data, risk assessments, and compliance requirements, effective prompt engineering can significantly enhance AI utility in credit evaluation, risk management, and customer communication.

Here’s how you can apply prompt engineering best practices specifically for B2B credit use cases, drawing on expert insights and practical examples:

1. Clarity and Specificity: Define Context and Desired Output

In B2B credit, ambiguity leads to poor credit risk assessments or irrelevant recommendations. Use prompts that clearly specify the task, data scope, and output format.

• Instead of:
• “Analyze this company’s creditworthiness.”
• Use:
• “Provide a detailed credit risk analysis for Company X based on its latest financial statements, payment history, and industry benchmarks. Highlight key risk factors and recommend a credit limit.”
  This level of specificity helps the AI focus on relevant data points and deliver actionable insights tailored to credit decision-making.

2. Use Delimiters and Structured Instructions for Complex Tasks

B2B credit assessments often involve multiple steps - financial analysis, risk scoring, scenario evaluation. Break down prompts with numbered lists or bullet points to guide AI systematically.

Example prompt structure:
“1. Summarize Company X’s financial health based on the income statement and balance sheet.
2. Assess payment behavior trends over the past 12 months.
3. Identify external risk factors such as market volatility or sector downturns.
4. Provide a final credit recommendation with justification.”

This approach reduces errors and improves precision in multi-faceted credit evaluations.

3. Create Personas and Scenarios for Contextual Nuance

Assign the AI a role to tailor responses with domain expertise. For B2B credit, prompt the AI as a credit analyst or risk manager.

Example:
“You are a senior credit analyst with 10 years of experience in B2B lending. Evaluate the credit risk of Company X, considering financial metrics, payment history, and industry outlook. Provide a risk rating and mitigation suggestions.”

This framing ensures the AI delivers nuanced, context-aware insights aligned with professional standards.

4. Encourage Analytical Reasoning and Meta-Cognition

Prompt the AI to weigh pros and cons, reflect on its reasoning, and self-evaluate its output to enhance decision quality.

Example:
“Analyze the credit risk factors for Company X. List potential strengths and weaknesses. Rate your confidence in the risk assessment on a scale of 1 to 10 and suggest any additional data needed to improve accuracy.”

This meta-cognitive prompting fosters deeper insights and highlights uncertainties in the credit evaluation, aiding better human oversight.

5. Tailor Prompts for Specific B2B Credit Use Cases

• Credit Limit Recommendations:
  “Based on Company X’s financial ratios and payment history, recommend a suitable credit limit and explain the rationale.”
• Fraud Detection and Compliance:
  “Identify any anomalies or red flags in Company X’s credit application that may indicate fraud or compliance risks.”
• Portfolio Risk Monitoring:
  “Summarize the credit risk exposure for our portfolio in the manufacturing sector, highlighting companies with deteriorating financial health.”

6. Example Prompt for B2B Credit AI Use

You are a B2B credit risk specialist. Review the following data for Company X:

• Latest financial statements (revenue, profit margins, debt levels)
• Payment history over the past 24 months
• Industry risk factors and economic outlook

Provide:

1. A summary of financial health
2. Identification of key credit risks
3. A risk rating (low, medium, high) with justification
4. Recommendations for credit limit and monitoring actions
   This prompt is clear, structured, and role-based, maximizing AI’s ability to deliver precise credit insights.

Why Prompt Engineering Matters in B2B Credit

• Improves Accuracy: Clear, detailed prompts reduce AI misinterpretation of complex financial data.
• Enhances Efficiency: Structured prompts speed up credit evaluations and decision-making workflows.
• Supports Compliance: Explicit instructions help ensure AI outputs align with regulatory and risk management standards.
• Enables Customization: Role-playing and scenario setting allow AI to adapt to specific credit contexts and business needs.

By mastering prompt engineering tailored to B2B credit, organizations can unlock AI’s full potential to streamline credit risk assessment, improve decision quality, and gain a competitive edge in credit management.

Stay tuned for the next installment, where we’ll share ready-to-use prompt templates for credit limit recommendations!

Disclaimer

The information and AI prompt templates provided in this series are intended for educational and illustrative purposes only. They do not constitute financial, legal, or professional advice. Always use your professional judgment and consult with relevant experts before making business decisions based on AI-generated outputs. If you choose to use real data with any AI engine or tool, be sure to anonymize all sensitive or personally identifiable information before uploading to protect privacy and comply with data protection regulations.

Join the Conversation

We want to hear from you! Have you tried using AI as an assistant in your B2B credit processes? What prompt strategies or tools have worked best for you, and where have you faced challenges? Share your experiences, questions, and suggestions in the comments below. Your insights will help us all learn and improve together as we navigate the evolving world of AI-powered credit management.

 

About This Article

This blog post was primarily generated using AI tools, including OpenAI's ChatGPT, which assisted in drafting the content based on best practices in prompt engineering for B2B credit professionals. While AI played a key role in creating the initial draft, the article has been thoroughly reviewed and edited by the author to ensure accuracy, clarity, and relevance to our audience.

The Credit Institute of Canada (CIC) recently held its second SQUADUP virtual session of credit professionals, igniting lively conversations around the adoption of artificial intelligence (AI) in credit management. The discussions highlighted several important themes: AI’s capacity to automate repetitive processes, improve decision-making through tailored scoring models, and function as a valuable educational resource—all while underscoring the indispensable value of human judgment. As a leading force in Canadian credit education and certification, the CIC remains at the forefront of these discussions, equipping credit professionals to enhance operational efficiency, strengthen client engagement, and adapt to the rapidly changing industry landscape.

AI as a Catalyst for Efficiency

AI offers significant potential to automate repetitive tasks, enabling credit professionals to focus on strategic priorities. By streamlining administrative duties like data entry and preliminary credit assessments, AI-driven tools free up time for building client relationships and addressing complex decisions. For instance, AI can process vast datasets to identify patterns, facilitating faster and more accurate risk assessments while leaving nuanced exception handling to skilled professionals.

The opportunity lies in using AI as a supportive tool that amplifies human capabilities. By automating routine processes, credit managers can strengthen their role as strategic advisors, fostering deeper client connections and driving business growth. This approach, championed in discussions led by the CIC, ensures technology enhances agility and responsiveness in credit management.

Enhancing Decision-Making with AI

AI’s ability to analyze large datasets opens exciting possibilities for credit scoring and risk management. Customizable AI models can align with a company’s specific risk tolerance, delivering tailored insights that improve decision-making. Synthetic data—artificially generated datasets—can also simulate market conditions, allowing professionals to test strategies without relying solely on external sources.

The CIC’s leadership in fostering dialogue about proprietary algorithms highlights the opportunity to combine AI-driven insights with human judgment. By building transparent systems that complement third-party tools, credit professionals can navigate complex decisions with confidence, adapting swiftly to market changes and enhancing organizational resilience.

AI as a Learning Ally

AI serves as a powerful teaching tool, guiding professionals in understanding market trends and refining strategies. From automating processes to delivering real-time insights, AI can support continuous learning. Credit reporting systems, for example, already leverage AI to provide actionable data, helping professionals make informed decisions without replacing their expertise.

The CIC’s emphasis on knowledge-sharing inspires opportunities to democratize learning. AI-driven tutorials and simulations integrated into training programs can bridge the gap between technical tools and domain expertise. This approach empowers credit professionals to stay ahead, aligning with the CIC’s vision of a skilled, tech-savvy industry.

The Human Edge in a Tech-Driven Future

Human interaction remains irreplaceable in credit management, particularly for complex decision-making, exception handling, and client relationships. Empathy, intuition, and domain expertise—qualities technology cannot replicate—are central to the industry’s success. The CIC’s leadership in these conversations highlights the opportunity to position AI as a partner that enhances these strengths, enabling professionals to deliver greater value.

Ongoing discussions fostered by the CIC can drive this vision forward. By sharing insights and exploring AI’s role collaboratively, credit professionals can identify best practices and innovate together, amplifying the industry’s voice and shaping its future.

A Path Forward

The integration of AI in credit management, as envisioned through the CIC’s progressive leadership, is about empowering professionals, not replacing them. By leveraging AI to automate tasks, enhance decision-making, and support learning, credit professionals can achieve new levels of efficiency and impact. Tailored training, collaborative dialogue, and a commitment to balancing technology with human expertise will pave the way for a thriving, tech-enabled future.

The credit industry, guided by the CIC’s forward-looking approach, has a unique opportunity to lead with innovation while preserving the human touch that defines its success. Let’s embrace this moment to build a stronger, smarter, and more connected credit management community.

Overcoming Technology Learning Curves

The Credit Institute of Canada (CIC) has already taken significant steps to equip credit professionals with practical technology skills, incorporating tools like Power BI and Power Query into its course offerings. While adopting such platforms can present challenges for those whose primary expertise is in credit, CIC’s existing curriculum and resources help bridge the gap between technical and credit-specific knowledge.

Looking ahead, the Institute is committed to expanding its educational content to include additional tools and skills, ensuring members remain at the forefront of industry developments.

About SQUADUP

SQUADUP is an invitation-only forum organized by the Credit Institute of Canada (CIC) for experienced credit professionals and emerging talents in the credit industry. The group provides a confidential space for thoughtful discussion and knowledge-sharing on emerging trends and challenges in credit management, supporting the professional growth and collaboration of its members.

Key Difference: The conventional role is reactive and manual, relying on established financial data and processes, while the futuristic role is proactive, tech-driven, and incorporates broader data ecosystems and sustainability. 

Conventional B2B Credit Manager (Today): 

•  Focuses on assessing creditworthiness using historical financial statements, credit scores, and trade references.
• Manages credit limits, payment terms, and collections processes manually or with basic software (e.g., Excel, ERP systems like SAP).
• Ensures compliance with current regulations (e.g., UCC filings, basic AML/KYC requirements).
• Mitigates risk through static policies and periodic reviews of client accounts.
• Emphasis on maintaining client relationships while enforcing credit policies.

AI-Driven Credit Portfolio Manager (2035):

• Leverages AI, blockchain, and predictive analytics for real-time credit risk assessment, moving beyond static data to dynamic, alternative data sources (e.g., IoT, social media activity).
• Optimizes portfolios proactively using advanced risk models and diversification strategies, rather than reacting to defaults.
• Incorporates emerging technologies like smart contracts and DeFi platforms to automate credit agreements and repayments.
• Integrates ESG metrics and sustainability into credit decisions, reflecting future regulatory and societal priorities.
• Focuses on fraud detection with AI-driven anomaly detection, elevating compliance to a proactive, tech-enabled level.
  
   

 

Introduction

The credit industry stands at a crossroads, as global trade dynamics, technological advances, and the need for continuous professional development converge to reshape the landscape. With trade tensions and tariff volatility now central to economic discussions, companies and credit professionals are facing new and unprecedented challenges. This paper synthesizes insights from a recent meeting of industry professionals, highlighting how tariffs disrupt cross-border trade, the strategies being employed to manage credit risk in this volatile environment, and the role of advanced education programs—such as the Executive Certified Credit Professional (ECCP) program—in preparing leaders for the evolving landscape of credit risk management.

Impact of Tariffs on Cross-Border Trade

Tariff volatility has emerged as a significant disruptor to both global supply chains and company creditworthiness. As trade policies shift, companies face increased costs for raw materials and transportation, which place additional pressure on operational efficiency and cost structures.

One of the key challenges discussed in the meeting was the complexity of global supply chains. Tariffs have driven up the cost of raw materials, affecting manufacturing industries across the world. In response, companies are faced with the difficult task of revising their pricing structures and adjusting payment terms to accommodate these additional expenses. This often involves shifting production locations to regions with lower tariffs or changing suppliers to avoid tariff-hit products.

Small and medium-sized enterprises (SMEs), often more vulnerable due to limited resources, face greater challenges in this new environment. With fewer options for hedging against tariff fluctuations and typically lower purchasing volumes, SMEs are at an increased risk of insolvency or financial instability.

To mitigate these challenges, professionals in the meeting discussed a range of strategies:

• Hedging strategies, particularly foreign exchange hedging, to protect against currency fluctuations exacerbated by trade tariffs.
• Diversifying markets to reduce dependency on specific regions or suppliers, ensuring that companies are not overly exposed to shifts in trade policies.
• Building resilience through long-term planning, which emphasizes flexibility and adaptability in supply chain management, allowing companies to weather unforeseen economic shifts and tariff changes.

These strategies underscore the need for companies to take a proactive approach to managing their credit exposure and to understand the broader implications of geopolitical changes on their financial health.

Mitigating Credit Risk

Managing credit risk in an environment marked by uncertainty requires innovation and foresight. Participants in the meeting emphasized the importance of proactive risk management strategies to maintain financial stability in the face of global volatility.

One approach discussed was the use of global operations to mitigate the risk of foreign exchange exposure. By spreading operations across multiple jurisdictions, companies can reduce the negative impact of sudden tariff increases or currency devaluations.

Additionally, regular portfolio reviews were highlighted as a crucial tool in managing credit risk. Frequent evaluations of customer portfolios allow businesses to identify potential risks early, particularly those arising from vendor financing practices that may expose companies to unstable international markets.

Shifting business models also became a key topic of conversation. Specifically, many participants discussed the advantages of distributor models, which allow companies to limit direct exposure to tariff fluctuations by working with third-party distributors who can absorb some of the supply chain risks. This approach not only helps to buffer against price volatility but also creates more agile credit management structures that can respond quickly to changing market conditions.

By adopting these forward-thinking strategies, credit professionals can safeguard their organizations from the financial risks associated with tariff volatility and other market disruptions.

Advancing Leadership Through Education

As the credit industry faces increasing challenges, the importance of continuous learning and professional development has never been more apparent. The Executive Certified Credit Professional (ECCP) program emerged as a key focal point during the meeting, with participants acknowledging its critical role in preparing the next generation of credit leaders to navigate complex global economic conditions.

The ECCP program offers several advantages:

• Advanced finance courses that enhance analytical capabilities, enabling professionals to better assess creditworthiness and financial stability.
• Leadership training that focuses on developing the skills necessary to navigate the increasingly complex economic landscape and address the challenges posed by tariff volatility and global trade shifts.

Participants in the meeting expressed strong enthusiasm for programs like ECCP, which they believe will equip credit professionals with the knowledge and skills necessary to lead organizations through an uncertain economic environment. Continuous learning and professional development are seen as vital to staying ahead of industry changes and fostering the leadership needed to tackle these challenges.

 

Community Insights and Collaboration

One of the standout features of the meeting was the strong sense of community that developed among participants. Professionals from various backgrounds shared insights and personal anecdotes, highlighting the diverse experiences within the industry. This collaborative environment underscored the importance of peer learning and knowledge-sharing, particularly in times of industry transformation.

The meeting also demonstrated the value of ongoing dialogue among credit professionals. As trade dynamics shift and economic conditions evolve, the ability to communicate, collaborate, and share strategies will be essential in overcoming future challenges.

Conclusion

The credit industry is navigating a period of significant transformation, driven by factors such as tariff volatility, supply chain disruptions, and evolving professional standards. To stay ahead of these changes, credit professionals must adopt innovative risk management strategies and invest in continuous education. Programs like the ECCP play a critical role in preparing professionals to lead in an uncertain economic environment.

By embracing these strategies and fostering collaboration within the industry, credit professionals can ensure that they are equipped to not only survive but thrive in the face of economic and geopolitical challenges. The insights shared in this meeting serve as a call to action for the credit industry to lead with resilience, agility, and foresight.

What would a professional managing credit be doing in 10 years?  

In 10 years, a credit manager could potentially have a more advanced and strategic role, with the title evolving in response to industry changes. The profession might shift from purely managing credit to integrating with broader financial risk and business strategy.

Here are some key developments that might shape the role:

1. Technology Integration: With AI, automation, and advanced data analytics, credit professionals will likely use sophisticated tools for risk assessment, trend analysis, and predictive modeling. They could shift from managing day-to-day credit decisions to focusing on high-level strategies to mitigate financial risk and improve cash flow management.
2. Data-Driven Decision Making: The use of big data and AI will make credit risk assessments faster and more accurate, reducing human errors and enhancing decision-making. Credit professionals will become more data-savvy, understanding complex financial indicators beyond traditional ratios.
3. Strategic Advisory Role: As businesses become more global and interconnected, credit professionals may take on advisory roles, guiding organizations in navigating financial risks, identifying opportunities for financing, and ensuring financial stability in volatile markets.
4. Collaboration with Other Functions: Credit professionals could work closely with legal, compliance, and IT departments to ensure that credit policies are aligned with new technologies and regulatory changes. They may also collaborate more with sales and marketing teams to optimize customer relationships while mitigating financial risks.
5. Focus on ESG: Given the growing emphasis on Environmental, Social, and Governance (ESG) factors, credit professionals may also assess creditworthiness based on a company's ESG performance, considering not only financial health but also long-term sustainability practices.

In short, credit professionals will likely evolve into multi-disciplinary risk managers with a broader scope of responsibilities, blending finance, data science, and strategic leadership. The title may no longer simply be "credit manager" but rather reflect their expanded, forward-thinking role in safeguarding financial health across organizations.

What do you see in your crystal ball?

Test

In this edition of "I ASKED AI," we dive into the day-to-day role of a Strategic Credit Analyst in the B2B context. Curious about how this key player in credit management navigates their responsibilities? I posed the question to AI to gain insights into the typical tasks, challenges, and strategies they employ to ensure businesses are making sound financial decisions and minimizing risks. Here's a look at how a Strategic Credit Analyst would spend their day in the fast-paced world of B2B credit. I'd love to hear from you—do you think this description is accurate? Feel free to share your thoughts and let me know if your day includes other tasks or responsibilities!

Morning: Data Analysis and Risk Assessment 

1. 8:00 AM – Review Overnight Updates
• Begin your day by reviewing updates from automated credit monitoring systems or AI tools.
• Check for alerts on customers whose financial health may have changed (e.g., late payments, declining credit scores, or industry risks).
• Prioritize high-risk accounts that need immediate attention.
2. 8:30 AM – Analyze Customer Portfolios
• Use advanced analytics tools to assess the financial health of key accounts.
• Evaluate payment trends, Days Sales Outstanding (DSO), and cash flow patterns to identify potential risks or opportunities.
• Prepare a report on customers nearing their credit limits or those who might benefit from adjusted payment terms to improve cash flow.
3. 9:30 AM – Strategic Risk Assessment
• Conduct scenario analyses for high-value customers or industries facing economic uncertainty.
• Use predictive models to forecast potential defaults or delays in payments based on macroeconomic trends.
• Develop recommendations for adjusting credit limits or terms for specific accounts.

Mid-Morning: Collaboration and Decision-Making

4. 10:00 AM – Cross-Functional Meeting
• Join a meeting with sales, finance, and operations teams to discuss key accounts and align on strategies.
• Provide insights into customer creditworthiness and suggest tailored payment terms that balance risk with sales growth.
• Collaborate on solutions for customers with overdue payments while maintaining strong relationships.
5. 11:00 AM – Customer Negotiations
• Participate in a call with a key customer to discuss their payment challenges.
• Negotiate revised payment terms or installment plans to ensure receivables are collected while supporting the customer’s financial stability.
• Use your knowledge of the customer’s financial position to propose win-win solutions.

Midday: Strategic Planning and Reporting

6. 12:00 PM – Lunch Break
• Take time to recharge and network with colleagues informally.
7. 1:00 PM – Strategic Credit Policy Development
• Work on refining your company’s credit policies based on recent trends in customer behavior, industry risks, and economic conditions.
• Incorporate ESG (Environmental, Social, Governance) factors into credit assessments for customers in industries transitioning toward sustainability.
8. 1:45 PM – Report Preparation
• Prepare a strategic report for senior management outlining:
• Key risks in the current receivables portfolio
• Recommendations for adjusting credit terms
• Opportunities for expanding into new markets based on low-risk customer segments
• Insights from predictive analytics tools

Afternoon: Technology and Process Optimization

9. 2:30 PM – Technology Review
• Review outputs from AI-driven credit scoring systems or blockchain-based payment platforms integrated into your AR processes.
• Identify areas where automation could further streamline workflows, such as faster invoice matching or enhanced fraud detection.
• Meet with IT or fintech vendors to discuss potential upgrades to your credit management systems.
10. 3:15 PM – Exception Handling
• Address unresolved issues flagged by automated systems (e.g., unmatched payments or disputes over invoices).
• Investigate discrepancies and work with internal teams or customers to resolve them efficiently.

Late Afternoon: Strategic Projects and Continuous Learning

11. 4:00 PM – Strategic Projects
• Work on long-term projects such as:
• Developing a framework for entering new markets with tailored credit policies
• Implementing a pilot program for blockchain-based real-time supplier payments
• Evaluating the impact of macroeconomic changes (e.g., interest rate hikes) on customer credit risk
12. 4:45 PM – Professional Development
• Dedicate time to continuous learning:
• Attend a webinar on emerging trends in B2B credit management (e.g., ESG integration, AI advancements).
• Read industry reports or case studies to stay updated on best practices.

End of Day: Wrap-Up and Planning Ahead

13. 5:30 PM – Wrap-Up Tasks
• Summarize key actions taken during the day and update stakeholders as needed.
• Plan tomorrow’s priorities based on outstanding issues or upcoming deadlines.
14. 6:00 PM – End of Day
• Log off with the confidence that you’ve contributed strategically to managing risk, supporting business growth, and optimizing processes.

In today’s fast-changing world, the saying “hire for slope, not for intercept” has never been more relevant. What you know—your INTERCEPT—matters less than how fast you can learn and adapt—your SLOPE. When the world changes rapidly and unpredictably, a steep slope is more valuable than a high intercept.

WHY SLOPE MATTERS MORE TODAY

Industries are being turned upside down. AI is reshaping the nature of work and redefining competitive advantage. Disruption is the norm. Just look at the last six months: OpenAI’s O1 and O3 reasoning models, Deepseek’s R1, and Perplexity’s Deep Research have upended how we use AI. In this environment, relying on what you already know is risky. To stay relevant, you must constantly reinvent yourself.

This is where Growth Mindset comes in. Coined by psychologist Carol Dweck, it is the belief that abilities can be developed through learning and effort. People with a growth mindset focus on improving (their slope), while those with a fixed mindset rely on their existing knowledge (their intercept).

TIPS FOR INCREASING YOUR SLOPE

1. Believe in Your Ability to Grow: Treat challenges as opportunities to learn, not as threats to your expertise. When I saw the AI wave coming five years ago, I jumped in head-first—and I’m having the time of my life.

2. Commit to Continuous Learning: Make it a habit to learn something new regularly. Whether through online courses, podcasts, books, or conversations with innovative people, always be a sponge.

3. Apply What You Learn: Use new knowledge and tools in your day-to-day work. Experiment, learn, and adapt. I use generative AI dozens of times a day—from writing and teaching to consulting and learning.

4. Stay Curious: Be restless. Ask questions, explore new fields, and embrace ideas that challenge your current beliefs. I wake up every day with questions and engage in conversations that spark new curiosity. The more I learn, the hungrier I become. Surround yourself with lifelong learners—their energy fuels your growth.

5. Use Intercept to Drive Slope: Your intercept—your accumulated knowledge and experience—can help you learn faster. For example, if you’re an expert in marketing, you can use that expertise to understand new digital marketing tools more effectively. What you know can accelerate your learning curve.

My key message - you must focus on learning, adapting, and growing. Don’t measure your career by where you are today. Don’t measure your expertise by what you know now. Measure yourself by your ability to learn and grow. In a world of rapid change, slope beats intercept every time.

Published with permission from Professor Mohanbir Sawhney, Kellogg School of Management, Northwestern University:  https://www.kellogg.northwestern.edu/faculty/directory/sawhney_mohanbir/
Prof. Sawhney is a globally recognized scholar, teacher, consultant and speaker in business innovation, modern marketing, and Artificial Intelligence applications in business. 

The recent bankruptcy protection filing of Picture Butte Feeders Cooperative (PBFC), Alberta's largest feeder association, has sent ripples through the Canadian livestock industry. This event highlights the interconnected challenges faced by creditors, cattle producers, and the broader supply chain. By examining the factors behind PBFC's financial struggles and extending the analysis to future risk management, we can uncover critical lessons for stakeholders in the livestock sector.

Part 1: What Led to Picture Butte Feeders Cooperative's Bankruptcy?

Tracing the Challenges from Consumers to Creditors

The financial troubles of PBFC can be understood by working backward through the supply chain—from consumers to creditors—revealing how systemic pressures compounded over time.

1. Consumer-Level Pressures

• Inflation and Spending Shifts: Rising living costs have forced consumers to prioritize essential spending, reducing demand for premium beef cuts. This has impacted revenue across the supply chain.
• High Retail Beef Prices: Record-high cattle prices, while beneficial for producers, have led to elevated retail beef costs. These prices have strained consumer affordability and limited market growth.

2. Livestock Producers' Challenges

• Labour Shortages: Labour challenges in cattle management and processing have increased operational costs, reducing profitability for farmers.
• Feed Costs and Supply Volatility: Past volatility in feed prices due to droughts and supply chain disruptions created financial strain on producers, limiting their ability to expand herds.
• Herd Decline: The Canadian beef herd is at its lowest in decades due to high slaughter rates of cows and heifers. This has constrained supply chain throughput and increased costs for feeder associations like PBFC.

3. Feeder Association-Level Struggles

• Internal Control Weaknesses: PBFC faced liquidity constraints and operational difficulties, potentially stemming from inadequate internal controls related to its credit facilities under Alberta's Feeder Associations Loan Guarantee (FALG)
• ProgramAggressive Credit Policies: Executives may have extended credit aggressively despite heightened economic risks, leading to defaults by members unable to repay loans.
• Systemic Defaults: Multiple member defaults likely strained PBFC’s financial health, creating a domino effect that led to insolvency.

4. Credit-Issuing Organization Issues

• Overextension of Credit: PBFC borrowed heavily under the FALG Program, making it vulnerable when members defaulted on loans or market conditions worsened.
• Lack of Oversight: Ineffective monitoring or auditing may have allowed financial issues to escalate before intervention.

Part 2: Lessons Learned and Strategies for Future Risk Management

The collapse of PBFC offers valuable lessons for creditors, cattle producers, and other stakeholders in the livestock industry. Proactive risk management and strategic adaptation are essential for navigating future challenges.

1. Adapting to Consumer Trends

• Monitor Consumer Preferences: Producers and creditors must track shifts toward alternative proteins and inflationary pressures that could reduce demand for traditional beef products.
• Diversify Markets: Expanding export markets—particularly in Asia—can reduce reliance on domestic demand and stabilize revenue streams.

2. Risk Mitigation for Producers

• Use Risk Management Tools: Producers should utilize tools like price insurance (e.g., calf, feeder, fed programs) to protect against market volatility and secure profitability during downturns.
• Control Costs: Efficiency improvements through technology adoption, optimized feed usage, and sustainable practices can reduce operational expenses.
• Rebuild Herds Strategically: Retaining replacement heifers and reducing slaughter rates of cows can help rebuild herds over time, ensuring long-term supply stability.

3. Best Practices for Creditors

• Stronger Credit Assessments: Creditors must implement stricter credit evaluation processes to avoid overextending credit to high-risk borrowers. This includes analyzing market conditions and borrower repayment capacity.
• Avoid Aggressive Lending: Lending policies should align with economic realities, avoiding risky extensions during periods of heightened uncertainty.
• Diversify Loan Portfolios: Lending across various agricultural sectors can reduce exposure to risks tied solely to cattle farming.

4. Strengthening Supply Chain Collaboration

• Vertical Integration: Collaborating across the supply chain (e.g., feed suppliers, processors) can stabilize input costs and secure market outlets for producers.
• Forward Contracts: Encouraging producers to use forward contracts or hedging strategies can lock in prices and reduce exposure to price volatility.

5. Policy Advocacy and Education

• Advocate for Supportive Policies: Stakeholders should push for policies that address trade uncertainties (e.g., tariffs) and promote sustainability in cattle farming.
• Promote Financial Literacy: Providing education on financial planning, risk management tools, and cost analysis can help producers make informed decisions.

Conclusion

The bankruptcy protection filing of Picture Butte Feeders Cooperative serves as a cautionary tale for Canada’s livestock industry. It underscores the importance of proactive risk management at every level—from consumers all the way up to creditors. By learning from these challenges and implementing strategic changes, stakeholders can build resilience against future disruptions while ensuring long-term sustainability in Alberta’s livestock sector.

As reported by BNN Bloomberg, Ford's CEO has warned that a 25% tariff on automotive imports could significantly disrupt the industry, driving up costs and potentially slowing down vehicle production. This warning highlights the ripple effect that such tariffs could have across the supply chain, impacting businesses both directly and indirectly tied to automotive manufacturing.

Ripple Effect on the Supply Chain

The tariffs could lead to financial strain not only for major automotive manufacturers but also for the vast network of suppliers supporting them. Smaller vendors, such as those providing office supplies, cleaning materials, or non-essential items like coffee machines and mats, could see reduced orders as automotive companies tighten their budgets. These businesses may experience delayed payments and lower demand as automotive companies reallocate their resources to core production needs.

Key suppliers, such as steel manufacturers and tool and die companies, which provide the raw materials and specialized equipment necessary for automotive manufacturing, might also face order slowdowns. As vehicle production halts or slows due to tariff-induced cost increases, these upstream suppliers could see their revenue shrink and face longer payment cycles, which could create a ripple effect further down the supply chain.

Mitigation Strategies

1. Reevaluate Credit Terms: Tighten payment terms and monitor the creditworthiness of both direct automotive clients and the smaller suppliers within your own supply chain to mitigate potential payment delays.
2. Diversify Clientele: Explore business opportunities in industries outside of automotive manufacturing to minimize the impact from any downturn in the sector.
3. Monitor Financial Health: Stay vigilant about the financial health of clients throughout the automotive supply chain, including smaller vendors, to anticipate any liquidity issues before they result in payment defaults.

By taking these proactive steps, B2B credit professionals can position themselves to better navigate the potential disruptions caused by tariffs in the automotive industry.

Source: BNN Bloomberg

Introduction: Understanding the Basics

Ever stumbled upon the terms "capital subscribed" and "capital paid" in a company's financial statements and felt a little lost? You're not alone. These phrases, while seemingly straightforward, can have significant implications, especially when a company faces financial difficulties. Understanding them is crucial for creditors trying to understand their potential for recovery. This post aims to demystify these concepts and shed light on their importance specifically for creditors.

Defining Key Terms: The Pizza Analogy

Let's start with the basics. Imagine a company as a pizza. The authorized share capital is the whole pizza – the maximum amount of ownership the company is allowed to sell. The issued share capital is the number of slices they've actually cut and made available to investors. Now, subscribed capital represents the slices people have committed to buy, while paid-up capital is the number of slices they've actually paid for.

Subscribed vs. Paid: The Crucial Difference

So, what's the difference? Subscribed capital is a promise, while paid-up capital is the real deal. It's the actual money the company has received from investors in exchange for those ownership slices (shares). Investors agree to buy shares at a certain price, and once they've paid that price, they have "paid-up" their subscribed capital.

Implications for Creditors: Priority and Recovery

Why does this matter, especially for creditors? In a bankruptcy scenario, creditors have a higher claim on the company's assets than shareholders (the pizza eaters). Paid-up capital represents the shareholders' investment – their slice of the pizza. After all the creditors are paid, if there's any pizza left, then the shareholders get a share, based on their ownership percentage.

Unpaid Subscribed Capital: A Potential Lifeline

Here's where things get interesting for creditors. If a company's assets aren't enough to cover all its debts, the bankruptcy trustee might come knocking on the shareholders' doors for any unpaid subscribed capital. Remember those slices people committed to buy but haven't paid for yet? They might be legally obligated to pay up to help satisfy the creditors. This is a crucial point for creditors to understand – it represents a potential additional pool of funds.

Unpaid Subscribed Capital vs. Receivables: Understanding the Nuances

You might be thinking, "Isn't this just like unpaid invoices – uncollected receivables?" It's a good analogy, but there are important differences. Unpaid subscribed capital is tied to the company's equity, not its day-to-day operations like receivables. It's a commitment to invest, not a payment for goods or services. And the company (or the trustee) often has stronger legal grounds to pursue unpaid subscribed capital than regular receivables. For a creditor, this means the possibility of recovering more than just what the company directly owes.

Use Cases: When Unpaid Subscribed Capital Matters Most

While not every bankruptcy involves unpaid subscribed capital, it can be a critical factor, especially for companies with staged payments for shares, complex corporate structures, or in sectors like finance where regulatory capital requirements are stringent. Imagine a tech startup raising funds in rounds – investors might pay for their shares in installments. If the startup folds before everyone has paid their dues, creditors will be looking at that unpaid subscribed capital as a potential source of recovery.

The Creditor's Perspective: Assessing Recovery Potential

So, why should a creditor care? Understanding capital subscribed and paid helps you assess the potential for recovery in a bankruptcy. Are there significant amounts of unpaid subscribed capital floating around? This could significantly impact the funds available to satisfy creditor claims.

Conclusion: A Crucial Piece of the Puzzle

Capital subscribed and paid might seem like just another line item in a financial statement, but it's a crucial piece of the puzzle, especially when the company's future is uncertain. It's a reminder that share ownership comes with responsibilities, and that understanding the fine print can make all the difference for creditors.

Disclaimer

So, there you have it! My journey into the world of "capital subscribed, paid, and unpaid," guided by Gemini AI. Remember, this is just for informational purposes, not professional advice.

Tariffs are a crucial element of international trade policy that have shaped economic relationships between nations for centuries. This primer will explore the nature of tariffs, their purposes, and their effects on global commerce.

 What Are Tariffs?

Tariffs are taxes imposed on imported goods when they cross national borders. The most common type is an import tariff, which is levied on products brought into a country. While export tariffs exist, they are rare, and in the case of the United States, they are constitutionally prohibited.

 Purpose of Tariffs

Tariffs serve several key functions:

1. Revenue Generation: Historically, tariffs were a significant source of government income, especially before the widespread adoption of income taxes.

2. Industry Protection: By making imported goods more expensive, tariffs can shield domestic industries from foreign competition.

3. Addressing Unfair Practices: Tariffs can be used to counteract subsidies or dumping by foreign countries that artificially lower the price of their exports.

4. National Security: Some tariffs are implemented to protect industries deemed crucial for national security.

 

Types of Tariffs

There are several types of tariffs:

- Ad Valorem Tariffs: Set as a percentage of the import's value

- Specific Tariffs: A fixed amount charged per unit of imported goods

- Tariff-Rate Quotas: Tariffs that increase significantly after a certain import quantity is reached.

 Who Pays for Tariffs?

Contrary to popular belief, tariffs are primarily paid by the importing businesses and, ultimately, passed on to consumers in the form of higher prices. This means that when a country imposes tariffs, its own citizens often bear the cost.

 Historical Context

The United States has a long history with tariffs, dating back to the country's founding. Tariffs were a primary source of government revenue until the 1930s when income taxes became more prominent. After World War II, there was a general trend towards reducing tariffs globally as part of efforts to promote international trade.

 Recent Developments

In recent years, tariffs have regained prominence as a policy tool. For example:

- In 2018, the United States imposed tariffs on various goods, including steel, aluminum, solar panels, and washing machines.

- These actions led to retaliatory measures from other countries, including Canada and the European Union.

- More recently, in 2025, the U.S. implemented new tariffs on imports from Canada, Mexico, and China, citing national security concerns related to immigration and drug trafficking.

 Economic Impact

The economic effects of tariffs are complex and often controversial:

- Consumer Costs: Tariffs typically lead to higher prices for consumers.

- Domestic Industry: While some industries may benefit from protection, others that rely on imported inputs may suffer.

- Global Trade: Extensive use of tariffs can lead to reduced international trade and potential economic slowdowns.

- Retaliation: Trading partners often respond with their own tariffs, potentially escalating into trade wars.

 Conclusion

Tariffs remain a powerful but contentious tool in international trade policy. While they can protect domestic industries and generate revenue, they also risk increasing consumer prices and sparking international trade disputes. As global economic relationships continue to evolve, the debate over the use and effectiveness of tariffs is likely to persist.

Insolvency cases in Canada have seen a notable rise in 2024, reflecting the growing financial challenges facing both consumers and businesses across the country. The trends highlight mounting economic pressure, driven by a combination of high interest rates, inflation, and other external factors.

Rising Consumer Insolvencies

Consumer insolvencies have reached significant levels in 2024, with the third quarter of the year seeing 34,588 filings. This marks a 13.5% increase compared to the same period in 2023, a concerning sign of rising financial distress among Canadians. Ontario, in particular, has experienced the sharpest rise, with a 20.2% increase in consumer insolvencies year-over-year, totaling 13,140 filings in the third quarter alone.

Business Insolvencies at a 15-Year High

Business insolvencies have also surged in 2024, with 1,312 businesses filing for insolvency in the third quarter. This represents the highest third-quarter volume since the 2009 Great Recession, indicating that companies are facing severe financial challenges. The first quarter of 2024 saw an even more dramatic rise in business insolvencies, with an 87.2% increase compared to the same quarter in 2023, marking the sharpest jump in 37 years.

A total of 2,003 businesses filed for insolvency in the first quarter, the highest volume since the 2008 financial crisis. Over the 12-month period ending March 31, 2024, business insolvencies increased by 56.7% compared to the previous year, reflecting the tough economic climate.

Small and Medium-Sized Businesses Hit Hardest

Small and medium-sized businesses (SMBs) have been particularly affected by these rising insolvencies. Unlike larger corporations, many SMBs lack the resilience and access to capital necessary to weather economic downturns. As a result, these businesses are struggling to survive in an environment characterized by higher debt-carrying costs and declining consumer demand.

The Factors Behind the Surge

Several factors are contributing to the surge in insolvencies in both the consumer and business sectors. High interest rates and inflation have placed significant financial strain on individuals and companies alike, making it more difficult to manage existing debt and navigate increased living costs. The end of pandemic-related government support programs has also left many without the safety net they relied on during the height of the COVID-19 crisis.

Additionally, new debt obligations, such as the repayment of CEBA (Canada Emergency Business Account) loans, have added further financial pressure on businesses, pushing many over the edge into insolvency.

A Continuing Trend of Financial Struggles

As 2024 continues, the financial strain on both individuals and businesses in Canada shows no signs of abating. With the combination of rising interest rates, higher inflation, and the loss of government support, many Canadians and businesses are finding it increasingly difficult to manage their financial obligations. This trend paints a picture of ongoing financial challenges for the foreseeable future.

Operational and Strategic Red Flags: A Closer Look at CVS and Lessons for Credit Professionals

Operational and strategic decisions made by a company can offer critical insights into its financial health. While changes in business direction can reflect innovation and adaptability, they often mask underlying distress. This section focuses on two key red flags observed in CVS’s operations—store closures and aggressive cost-cutting measures—and their implications for creditors and analysts.

1. Store Closures and Business Restructuring

Example

In recent years, CVS announced the closure of hundreds of retail locations as part of its strategy to pivot away from traditional retail operations and focus on health services, such as walk-in clinics and pharmacy benefits management. This move was framed as a way to "streamline operations" and adapt to changing consumer behaviors.

Warning Sign

Large-scale store closures can signal more than just strategic realignment. For creditors, this raises red flags about potential overextension or failure to sustain profitable operations in certain markets. Restructuring often reflects deeper financial or operational inefficiencies, and such closures may not immediately lead to cost savings due to associated expenses like lease terminations, severance payments, and asset write-offs.

What to Monitor

·        Announcements of closures: Regular press releases or earnings calls discussing closures, divestitures, or layoffs.

·        Strategic pivot rationale: Examine whether restructuring aligns with long-term strategy or is a reactive measure to cash flow constraints.

·        Impact on revenues: Monitor subsequent earnings reports to gauge whether closures result in improved margins or continued revenue decline.

Other Examples

Companies like Sears and J.C. Penney pursued similar closure strategies in their decline. While their aim was cost-saving, it highlighted weakening market positions, strained resources, and a failure to compete effectively with e-commerce competitors like Amazon.

2. Aggressive Cost-Cutting Measures

Example

CVS implemented several cost-saving programs across its operations, including layoffs, reduced capital expenditures, and operational efficiency initiatives. While these actions were part of a broader restructuring, they also hinted at potential liquidity concerns.

Warning Sign

For a company of CVS’s scale, drastic cost-cutting can signify immediate financial pressures or attempts to meet short-term targets at the expense of long-term value creation. This may erode employee morale, hinder innovation, and compromise service quality, potentially damaging brand equity and customer loyalty.

What to Monitor

·        Public filings: Look for restructuring charges, asset sales, or deferred investments in SEC filings and earnings reports.

·        Operational capacity: Assess whether cost-cutting leads to reduced customer satisfaction or operational inefficiencies.

·        Competitor analysis: Compare CVS’s strategies with peers like Walgreens or Amazon Pharmacy to understand market dynamics and whether CVS’s approach is defensive or proactive.

Other Examples

·        General Electric (GE) aggressively reduced costs in the 2010s, only to find itself struggling to maintain market share and product innovation.

·        WeWork slashed expenses during its post-IPO turmoil, reflecting cash flow problems and difficulty maintaining its valuation narrative.

Key Lessons for Credit Professionals

1.        Scrutinize restructuring announcements: These can indicate underlying financial distress, even when labeled as strategic pivots.

2.        Evaluate cost-cutting in context: Temporary savings that compromise long-term sustainability are concerning for creditors.

3.        Track industry trends: Operational shifts may reflect broader market challenges rather than company-specific weaknesses.

By closely monitoring these red flags, creditors can better understand the risks involved in extending credit to companies like CVS and proactively safeguard their interests.

 

Case Study: CVS Health – Early Financial Warning Signs for Credit Managers

Introduction

In recent months, CVS Health has faced mounting financial pressures, presenting credit managers with a real-world case study in identifying early warning signals of client risk. For those with a vested interest in CVS’s financial health, such as suppliers and other stakeholders, understanding these indicators is critical for maintaining sustainable credit practices. This article explores CVS’s financial red flags, highlighting what credit managers should watch for and referencing insights from a recent analysis shared in the memo to the CFO, available on the CIC blog site.

Background on CVS Health

CVS Health has long held a prominent position in the healthcare and retail industries, expanding its operations to include healthcare services, retail pharmacy, and insurance through its acquisition of Aetna. This diversification strategy placed CVS at the forefront of integrated healthcare and retail services but also led to a significant increase in debt and operational costs. As the company works to balance its vast operational scope with fiscal responsibility, credit managers should examine key indicators that point to potential risks.

Section 1: Financial Health Signals—What Was Visible Early On?

While CVS’s financial challenges became more apparent over time, certain financial health signals were evident early on and could have served as indicators for those extending credit. Key financial warning signs included a mounting debt load, shrinking profit margins, and erratic cash flow patterns. Here’s a breakdown of each signal and its relevance to credit risk:

1. Increased Debt Load

• Example: Over recent years, CVS’s debt burden grew significantly, especially following its acquisition of Aetna. The deal transformed CVS into a healthcare conglomerate but left it with a substantial debt-to-equity ratio.
• Warning Sign: When a company takes on substantial debt, especially to fund large acquisitions, it may struggle with debt servicing, putting a strain on liquidity and operational flexibility. This often results in prioritizing debt payments over other financial obligations, such as vendor payments.
• What to Monitor: Credit managers should track debt-to-equity ratios, interest coverage ratios, and overall debt trends. Any rapid increase in debt, especially when combined with decreased revenue or profitability, can be a red flag that a client may face difficulties meeting its obligations.

2. Profit Margin Compression

• Example: CVS faced tightening profit margins in its retail and pharmacy sectors, in part due to increased competition and rising operational costs. This decline affected its ability to generate consistent profits across its divisions.
• Warning Sign: A shrinking margin may indicate that a company is either failing to manage costs effectively or struggling to keep up with competitive pressures. For credit managers, reduced profitability means there’s less income available to cover fixed obligations, increasing the likelihood of delayed or defaulted payments.
• What to Monitor: Regular monitoring of gross and net profit margins, EBIT (Earnings Before Interest and Taxes), and EPS (Earnings per Share) can signal potential struggles. A steady decline in these areas often reflects reduced financial resilience, warranting a reassessment of credit terms.

3. Cash Flow Variability

• Example: CVS experienced fluctuations in free cash flow as it juggled debt servicing, operational costs, and fluctuating revenue from its retail operations. This variability created unpredictability in its cash reserves, posing liquidity challenges.
• Warning Sign: Cash flow irregularities signal possible liquidity constraints, especially if free cash flow trends downward over time. Inconsistent cash flow can mean delayed payments to creditors, impacting the financial stability of those in the supply chain.
• What to Monitor: Observing cash flow from operations, the working capital ratio, and accounts payable turnover can reveal changes in a client’s financial stability. Consistent cash flow dips are a cautionary signal for suppliers and creditors alike.

Key Takeaways for Credit Managers

By analyzing CVS’s financial health indicators, credit managers can derive valuable lessons applicable to other high-risk clients:

• 1. Maintain Vigilance on Debt Increases: Large acquisitions funded by debt can result in unsustainable financial burdens. Monitoring debt growth, especially in conjunction with financial statement reviews, allows credit managers to identify clients under stress early.
• 2. Prioritize Profitability Metrics: Tracking margins and profitability over time helps identify if a company can continue to support debt obligations, manage operational costs, and maintain profitability.
• 3. Cash Flow Consistency is Critical: Cash flow irregularities impact a company’s ability to meet short-term obligations, so monitoring cash flow statements provides a direct insight into a company’s liquidity health.

Conclusion

Understanding these financial warning signs is essential for managing credit risk effectively. By recognizing early signals like debt spikes, margin compression, and cash flow variability, credit managers can proactively adjust credit terms, set safeguards, and prepare for potential payment delays. For an in-depth analysis of CVS’s financial standing and additional recommendations for credit managers, see the memo to the CFO on the CIC blog site.

Next week, we’ll explore the operational and strategic red flags credit managers should watch for, using CVS as an ongoing example of navigating risk in today’s complex financial landscape.

As AI becomes an integral part of business operations, understanding the building blocks of AI—data, algorithms, machine learning (ML), and neural networks—enables professionals, including those in B2B credit, to see how they can support and leverage this technology in their work. Here’s a closer look at these foundational elements and how B2B credit professionals, especially those working for companies that extend credit to other businesses, can actively contribute to each component.

For a deeper dive into how B2B credit professionals can shape AI and drive smarter risk analysis, read the full article on our private blog. Join the conversation and share your thoughts—your insights are invaluable as we explore the future of AI in credit!

Data: The Foundation of AI

Data is the core of any AI model. The quality and relevance of data directly impact how well AI performs its tasks. For B2B credit professionals, this means focusing on collecting and managing high-quality data related to customer behavior, payment histories, industry trends, and macroeconomic factors that influence credit risk.

How B2B Credit Professionals Can Contribute:

- Data Collection: Ensure accurate, comprehensive data collection on clients, payment behavior, industry credit trends, and financial performance metrics. This might include setting up systems to capture customer credit application details, transaction histories, and payment timelines.

- Data Standardization: To make data useful for AI models, it should be structured and standardized. Credit professionals can work with data teams to define consistent formats and categories for financial records, risk ratings, and client information.

- Data Enrichment: Beyond internal records, supplementing data with external sources (e.g., economic indicators, industry reports, news) can help enhance the AI model's predictive power.

- Data Quality Assurance: Regularly audit and clean data to remove duplicates, correct errors, and ensure consistency. High-quality data reduces biases and inaccuracies, resulting in more reliable AI predictions.

Algorithms: The "Recipe" of AI

Algorithms are sets of rules that guide AI in processing information. In credit risk analysis, algorithms can determine how the AI system identifies patterns in creditworthiness, payment timelines, and potential risks.

How B2B Credit Professionals Can Contribute:

- Collaborate on Algorithm Development: Work closely with data scientists to help define the credit-specific factors that should be included in risk algorithms. For instance, credit professionals may recommend weighting certain variables (e.g., payment history, debt-to-income ratio) more heavily based on industry experience.

- Define Credit Risk Variables: Credit professionals bring industry insights that can help identify key metrics and ratios (like DSO, DBT, and current ratio) relevant to assessing client creditworthiness. Their input can enhance algorithms by making them more industry-specific.

- Identify Patterns and Anomalies: By analyzing real-world credit cases, credit professionals can share insights on unique risk indicators, helping data scientists fine-tune algorithms to capture subtleties like seasonality in payments or red flags in financial reports.

Machine Learning (ML): Teaching AI to Improve Over Time

Machine learning, a subset of AI, involves training models with data so they can make predictions and improve over time. For instance, ML models in B2B credit could predict client payment delays based on past payment patterns or assess the risk of default based on current financial conditions.

How B2B Credit Professionals Can Contribute:

- Train AI Models: Provide labeled data that the AI can learn from. For instance, tagging historical client data as “high risk” or “low risk” helps train the ML model to distinguish between different risk levels.

- Support Model Validation: Once an AI model is trained, it needs to be validated on new data to ensure accuracy. Credit professionals can assess and validate model outputs, identifying if the predictions align with actual outcomes and adjusting the model as needed.

- Feedback Loop Creation: Continuously provide feedback to the AI system. By sharing insights on false positives/negatives or unusual risk cases, credit professionals can help improve the model's accuracy over time.

Neural Networks and Deep Learning: Mimicking Human Decision-Making

Neural networks and deep learning models allow AI systems to handle complex, multi-dimensional data—important in credit risk analysis where multiple factors and their interactions impact decisions.

How B2B Credit Professionals Can Contribute:

- Participate in Complex Model Building: Offer insights into complex credit scenarios that involve numerous variables, helping shape neural networks that capture non-linear relationships. For example, neural networks might analyze how a company’s revenue growth rate interacts with its debt levels to predict default risk.

- Assist in Multi-Layer Analysis: Credit professionals can suggest multi-layered data points to be included in the model, such as combining payment history with broader market signals, which neural networks can use to generate more comprehensive insights.

- Contribute to Scenario Analysis: Provide scenarios from past credit evaluations to help neural networks learn how to handle nuanced cases. This can improve the model's ability to make predictions across various credit conditions.

Practical Steps for B2B Credit Professionals

To contribute effectively, B2B credit professionals should consider a few practical actions:

1. Work Closely with Data Teams: Establish strong communication with data scientists and IT departments to align on data needs, feedback on model accuracy, and continuous improvements.

2. Promote a Data-Driven Culture: Emphasize the importance of data quality within the credit team and advocate for data literacy, ensuring team members understand the value of accurate data for AI insights.

3. Stay Informed on AI and ML Trends: Take part in training and workshops on AI basics, data science, and ML to understand how AI could be applied in their work area, making collaboration with data teams more effective.

4. Experiment with Predictive Models: Start small by testing predictive models on lower-risk accounts or specific data sets to assess accuracy, identify gaps, and gain confidence before scaling AI usage.

5. Measure and Report AI Success: Track the success of AI-driven initiatives, like reductions in default rates or faster risk assessments, and share these results within the organization to promote AI adoption.

Final Thought: A Collaborative Future

As AI becomes more integral to credit risk analysis, B2B credit professionals play a crucial role in shaping the technology to meet industry needs. By focusing on data quality, contributing to algorithm development, and assisting with model validation, credit professionals can help build smarter AI systems that support better credit decisions, manage risk effectively, and ultimately drive business growth.