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    The Building Blocks of AI
    By
    Nawshad Khadaroo, CCP (Emeritus)
    on
    October 29, 2024

    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.

     
    Last reply on October 31, 2024 by Celine Esteves
    2 replies

Recent Entries

Profit Increased, Why Is Cash Still Tight
Part 2 Case Quiz

Use the article and the Maple case to answer all six questions. Select the best answer in each case.

1. Maple sells $100,000 of inventory that cost $80,000 on 60-day credit terms. The customer has not yet paid. Which combination is correct immediately after the sale?

2. Maple's receivables increased by $400,000 and inventory increased by $250,000, while accounts payable increased by $150,000. How much of the selected working-capital increase had to be financed from other sources?

3. Maple's simplified calculation produces negative $20,000 after adjusting EBITDA for the stated changes in receivables, inventory and payables. Which statement is most accurate?

4. Company A has current assets of $900,000, current liabilities of $600,000 and EBITDA of negative $100,000. Which interpretation is supported by these figures?

5. Company B has current assets of $600,000, current liabilities of $800,000 and EBITDA of $300,000. What information is most important before interpreting its negative working capital?

6. During the year, Company C's receivables decreased by $120,000, inventory increased by $40,000 and accounts payable decreased by $30,000. Ignoring all other items, what is the net cash effect of these three changes?

Please answer all 6 questions before viewing the results.

Solution Key: 1-C, 2-D, 3-B, 4-A, 5-C, 6-D.

Question 1 — Answer: C

Gross profit is $100,000 − $80,000 = $20,000. Because the sale is on credit and remains unpaid, cash collected is $0 and accounts receivable is $100,000.

Question 2 — Answer: D

Receivables and inventory absorbed $400,000 + $250,000 = $650,000. The $150,000 increase in payables financed part of that amount. The remainder is $650,000 − $150,000 = $500,000.

Question 3 — Answer: B

The negative $20,000 is based only on EBITDA and the three stated working-capital changes. It excludes interest, taxes, capital expenditures, debt repayments and other cash-flow items, so it is not complete operating cash flow or the exact change in cash.

Question 4 — Answer: A

Working capital is $900,000 − $600,000 = positive $300,000. EBITDA is negative $100,000. Working capital measures a position at a date, while EBITDA measures performance over a period; the two results can coexist.

Question 5 — Answer: C

Working capital is $600,000 − $800,000 = negative $200,000, while EBITDA is positive $300,000. The key question is whether Company B collects cash through its operating cycle before its current obligations must be paid. Without that timing information, the negative balance cannot be interpreted reliably.

Question 6 — Answer: D

A $120,000 decrease in receivables releases cash. A $40,000 increase in inventory uses cash, and a $30,000 decrease in payables also uses cash. The net effect is $120,000 − $40,000 − $30,000 = a $50,000 cash source.

Same Business, Different Credit Decision
Part 1 Case Quiz

Use the Maple Industrial Supply case to answer all six questions. Select the best answer in each case. The questions require both calculation and credit judgment.

1. If Maple's accounts receivable had increased at the same rate as its sales, approximately what would the current receivables balance have been?

2. After considering the increases in receivables, inventory and accounts payable, how much additional working capital did Maple have to finance from sources other than the increase in payables?

3. Which pair of competing explanations should the credit professional test before interpreting Maple's higher working-capital balances?

4. Which fact would most reasonably explain why the bank and supplier could reach different credit decisions without either assessment being inconsistent?

5. Which additional information would best help distinguish growth-related receivables from a deterioration in collections?

6. Maple's sales increased by approximately 14.3%, while its accounts receivable increased by approximately 57.1%. What is the most defensible interpretation of that comparison?

Please answer all 6 questions before viewing the results.

Solution Key: 1-B, 2-D, 3-C, 4-A, 5-B, 6-C.

Question 1 — Answer: B, $800,000

Sales growth = ($4,800,000 − $4,200,000) / $4,200,000 = approximately 14.3%. Applying the same growth rate to prior-year receivables gives $700,000 × 1.143 = approximately $800,000. Actual receivables are $1,100,000, about $300,000 above the amount implied if receivables had grown in line with annual sales. That difference requires explanation; it does not establish the cause.

Question 2 — Answer: D, $500,000

Increase in accounts receivable: $1,100,000 − $700,000 = $400,000. Increase in inventory: $900,000 − $650,000 = $250,000. Combined increase: $400,000 + $250,000 = $650,000. Less financing provided by the $150,000 increase in accounts payable: $650,000 − $150,000 = $500,000. This simplified amount excludes other assets, liabilities and cash flows.

Question 3 — Answer: C, Growth-related investment versus slower conversion of assets into cash

Higher receivables and inventory can result from healthy growth that requires more working capital. They can also result from slower collections or slower inventory movement. The figures alone do not distinguish between these explanations, so both should be tested.

Question 4 — Answer: A, Their exposures, information access and legal protections may differ

The bank and supplier are deciding on different facilities and may have different reporting rights, security, repayment arrangements and direct operating information. Those differences can support different decisions about the same customer without making either analysis internally inconsistent.

Question 5 — Answer: B, Comparative receivables aging and collection-day trends

An aging comparison shows whether the increase is concentrated in current receivables or in older unpaid balances. Collection-day trends help determine whether customers are taking longer to pay. Together, they help separate growth in sales from deterioration in collections.

Question 6 — Answer: C, The difference warrants investigation into timing, aging and sales patterns

Receivables grew materially faster than annual sales, which is a valid signal for further review. The comparison does not prove customer inability to pay or establish the amount of financing required. Timing of recent sales, seasonality, acquisitions and collection performance could affect the result.

Part 2: What Is the Bond Market Telling Me About My Customer?
Quiz: Can You Read the Signals?

This time, the questions are harder than the first quiz. They test application, not simply whether you remember that price and yield move in opposite directions.

1. A recession begins and investors rush into U.S. Treasuries as a safe haven. What would you normally expect?

2. Six months ago, a customer's bond yielded 6% while a comparable government bond yielded 4%. Today, the customer's bond yields 9% while the government bond yields 3%. What is the strongest credit signal?

3. Company A's bond yield rises from 6% to 9%, while the comparable government yield rises from 4% to 7%. What happened to the approximate credit spread?

4. A customer has $500 million of cash but also substantial debt, negative free cash flow and a large bond maturity next year. Which question is most important to investigate?

5. A company's bonds suddenly fall sharply in price while Treasury yields are declining. The company is still paying your invoices on time. What is the most appropriate response from a credit professional?

Please answer all 5 questions before viewing the results.

Answers: 1-B, 2-C, 3-B, 4-B, 5-C.

The point of this second quiz isn't just whether you scored 5/5.
It's whether you can move from:
"I know how bonds work"
to:
"I know what the bond market might be telling me about a business."