Credit risk analysis is essential in determining if a customer is at risk of defaulting on payments. Carrying too many high-risk customers, or even just a few large-transaction customers who are a potential default risk, can be very detrimental to your business. Any time you invoice clients after providing goods or services, you expose your business to late payment risks that can disrupt cash flow.
Conversely, overly conservative risk policies can result in opportunity costs if you restrict the credit extended to a reliable customer who would be willing to buy more. Striking the right balance is critical to maximizing your company's bottom line.
Continuously improving your credit risk analysis techniques to capture the full picture of a customer or potential customer – using behavioral snapshots from their recent past as well as regular updates on their ongoing financial activities – is key to detecting warning signs early and avoiding potential losses.
What is Credit Risk Analysis?
Credit risk analysis is the means of assessing the probability that a customer will default on a payment. To determine the creditworthiness of a customer before you extend trade credit, you need to understand their reputation for paying on time and their capacity to continue to do so.
When it comes to analyzing the credit risk of a new customer, smart businesses use a series of strategies to gain a more complete view. That means first assessing the financial position data-driven tools that quickly capture trade information.
Running a business credit report, which illustrates a customer’s ability to pay invoices based on payment history and public records, is an important next step. Requesting trade references from the customer’s bank and lenders, as well as businesses or suppliers that already extend trade credit to that customer, is also good practice.
While these practices can help you mitigate risk, it’s important to note that potential clients are likely to provide companies they pay on time as references and omit companies with which they have a less-than-perfect record.
Companies like credit insurers that specialize in payment risk can reduce this uncertainty since they have unique oversight of millions of buyer relationships and covered transactions, not just a select few. Calculating a client’s debt-to-income ratio shows you how the company’s obligations stack up against its earnings - and the lower the number, the higher their creditworthiness. Finally, when assessing an international client, it is important to review anycountry-specific credit risks, which can be affected by fluctuations in currency exchange rates, economic or political instability, the potential for trade sanctions or embargos, and other issues.
What Are Credit Risk Models?
Credit risk analysis models are quantitative tools used to assess the likelihood that customers will default on payments or other financial obligations. B2B enterprises rely on these models to determine whether to extend trade credit to prospective clients – and if so, how much and on what terms.
These models evaluate a variety of internal and external factors, including clients’ payment history, industry trends, and geopolitical conditions. With continuous advancements in high-level programming languages, machine learning and artificial intelligence (AI), firms now have access to a rapidly expanding risk analysis toolkit. As a result, creditors are able to make more accurate predictions and refine their trade policies to mitigate losses from bad debts without inhibiting sales growth.
Influential Factors in Credit Risk Modeling
Accurate forecasting is essential to effective credit risk analysis. Here are a few key factors that businesses should assess:
Loss Given Default
Loss given default (LGD) is the proportion of an asset that a creditor would be unable to recover if a customer defaults on payment.
There are several methods of calculating LGD, but the simplest way is to divide the loss amount by the original amount and convert it into a percentage. For example, if Firm X extends $100,000 in credit to Firm Y and would be able to recover a total of $60,000 by liquidating inventory or pursuing other mitigation strategies, the LGD would be 40%.
Exposure at Default
Exposure at default (EAD) is the gross sum of money that a creditor would lose if a customer defaults on payment. Unlike loss given default (LGD), EAD doesn’t consider collateral or other means of recovery, so it’s often considered a more conservative metric. It’s also a dynamic number, which means that it changes as a customer pays down the outstanding amount.
In most trade credit situations, EAD is equal to the current amount due (in the case of revolving trade credit, the math gets trickier). Building on the previous example (where Firm X extends $100,000 in credit to Firm Y), if Firm Y makes a partial payment of $10,000 on day 15, the EAD would be $90,000 on day 15.
Probability of Default
Probability of default (PD) is the likelihood that a customer will default on payment within a specified period. For businesses, PD may consider a number of variables, like cash flow, revenue growth, business credit history, industry trends, and broader economic conditions like unemployment and inflation.
There are numerous ways to calculate PD, most of them employing advanced statistical approaches like logistic regression and neural networks. Often, PD is expressed as a percentage and relates to a one-year time horizon. So, if Firm Y has a PD of 5, that indicates they have a 5% chance of defaulting on payment in the next year.
3 Ways to Take Credit Risk Analysis to the Next Level
Improving your credit risk management process can improve your business’s overall financial stability. Taking your credit risk analysis to the next level will deliver greater insight into whether a customer is struggling - even if they are currently paying you on time.
Being aware of these risks can help you avoid the repercussions of a sudden and significant nonpayment. A key to improving your credit risk analysis is having access to experts who understand local and international markets and their risks and can help you identify signs of trouble or potential disruptions early.
Here are three ways to improve your credit risk analysis:
Refine Credit Scoring Techniques
While credit scoring helps paint a picture of a customer’s creditworthiness based on their financial history, it does not tell you everything you need to know about probability of default. Those with low credit scores may be at a higher risk for nonpayment, based on their history of default or other financial issues, but a good credit score does not necessarily mean a customer is a low risk.
Even with a stellar credit history, any business or individual faced with significant or unexpected economic hardship is at risk of default. That’s why refining your credit-scoring technique is an important part of enhancing your credit risk analysis.
Consider including the following criteria when determining the creditworthiness of a customer:
- A customer’s recent most up-to-date financial activities, including their cash flow status
- External factors like economic activity and stability in the customer’s geographic area, prevailing interest rates and the financial performance of closely related industries
- Market, industry and performance trends analysis
Incorporate Trend Analysis into your Process
Trend analysis is an important component of credit risk analysis. Instead of only looking at a client’s past credit history, dig deeper to understand their potential as a credit risk. Sound credit risk analysis techniques include understanding these trends:
- The client’s business performance: Is it stable, improving or declining, especially as compared to competitors’ performance trends
- The market environment: Is it improving or declining?
- National and global economic trends: Which trends are pertinent to the client’s industry?
- Changes in a client’s debt-to-income ratio: How does their current ratio compare to previous years?
Embrace New Technology and Tools
When it comes to keeping track of all of the variables that contribute to a customer’s creditworthiness and risk profile, employing the right technology is critical.
Customer relationship management (CRM) tools offer quick access to transaction histories and trends withfor current customers - and this data can sometimes indicate emerging credit risks.
Machine learning can enhance your credit risk modeling capabilities, allowing for the automatic detection of risks based on algorithms and large datasets. This technology compares your customer’s specific credit profile to the profiles of many others to determine probable risk.
Integrating machine learning and artificial intelligence (AI) into your customer management and credit risk management processes allows for continuous monitoring of customers’ relationships with you and their overall financial health, as well as worrisome internal and external trends.
With Allianz Trade, businesses can access robust technology-driven data to sharpen credit risk analysis. This data can help you avoid bad debts and safely expand sales to new and existing customers.