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:
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:
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:
Based on this information, provide:
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:
Strengths:
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
5. Monitoring Plan
6. Red Flags to Watch
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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:
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
Credit Review for Apex Manufacturing
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.
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.
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.]
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.
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.
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
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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:
- 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]
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.
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:
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.
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.
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.
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.
You are a B2B credit risk specialist. Review the following data for Company X:
Provide:
Why Prompt Engineering Matters in B2B Credit
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!
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.
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):
AI-Driven Credit Portfolio Manager (2035):
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:
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:
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:
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?
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
Mid-Morning: Collaboration and Decision-Making
Midday: Strategic Planning and Reporting
Afternoon: Technology and Process Optimization
Late Afternoon: Strategic Projects and Continuous Learning
End of Day: Wrap-Up and Planning Ahead
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
2. Livestock Producers' Challenges
3. Feeder Association-Level Struggles
4. Credit-Issuing Organization Issues
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
2. Risk Mitigation for Producers
3. Best Practices for Creditors
4. Strengthening Supply Chain Collaboration
5. Policy Advocacy and Education
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
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.
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.
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.
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.
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.
· 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.
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.
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.
· 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.
· 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.
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
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
2. Profit Margin Compression
3. Cash Flow Variability
Key Takeaways for Credit Managers
By analyzing CVS’s financial health indicators, credit managers can derive valuable lessons applicable to other high-risk clients:
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.
- 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.
- 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.
- 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.