With the average bank spending between $200 million and $500 million a year on credit risk modelling – understanding how to make it more effective is key.
Credit risk models enable organisations to quantify the probability of a customer defaulting on a payment, and how to optimise the credit function to minimise risk, and thereby maximising reward. They draw on vast amounts of historical data to build a view of the potential credit risk profile of any given customer, by analysing both minor and major features and contributions that attribute to risk.
Financial Institutions (FIs) are required to adhere to certain standards and regulations relating to credit risk, such as the recently updated standards for establishing capital requirements for the banking industry, officially unveiled by the Basel Committee on Banking Supervision (BCBS), known as Basel IV. To comply with these new regulations, FIs may need to change their risk management systems and procedures, and will be required to improve their internal models so they can get a more precise forecast and calculate any potential losses brought on by credit risk.
There is a clear requirement for FIs to invest in credit risk models to comply with these regulations, but they’re not the only organisations that can benefit from this type of model. For instance, retail companies, mobile providers, energy companies and other organisations that offer credit to customers can use them to evaluate their creditworthiness before providing them with services or credit, helping to minimise the risk of non-payment, reduce bad debt and optimise the company’s credit policies and strategies.
There are three main types of credit risk model, which include:
Default Probability Models: Default probability models are used to predict the likelihood of default for individual borrowers or portfolios. These models are typically based on statistical analysis of historical data, and can be used to estimate the probability of default using different scenarios.
Exposure at Default Models: Exposure at default models are used to evaluate the amount of capital the lender is at risk of losing if a customer defaults. Loss
Given Default Models: Loss given default models are used to predict the potential losses that might arise from defaults, this model also considers the potential recovery on default. Recovery is any way the lender can gain back some of their losses.
Integrating some or all of these types of models, depending on the needs of the business, has clear benefits. For instance, they can improve the accuracy of identifying customers more likely to attract risk to the company, and this increased accuracy can improve their credit decisioning, leading to a reduction in lost revenue. This can be achieved through a reduction in false positives, such as identifying customers who don’t pose significant risk and refusing or reducing credit, or by allowing customers credit that do in fact pose significant risk and losing revenue by default.
Additionally, better evaluation of a client’s risk facilitates better approval or rejection decisions, keeping to the credit risk appetite of the company (such as 95% of the portfolio must be AAA rated). Credit risk score re-calculations need to be run periodically for the whole of the portfolio to enable credit risk portfolio management, so if clients begin to shift from higher ratings to lower ones, a prompt will be activated to stop onboarding lower rated customers and start making changes to the credit policy for existing clients, to ensure that the business is not put at risk.
Credit risk models also help lenders identify high risk borrowers and take proactive steps to mitigate their risk exposure. This can include adjusting interest rates, requiring collateral, or rejecting credit applications altogether. By using this data driven approach, businesses can make informed decisions on the collateral/repayment terms etc, when lending to the high-risk borrowers.
Lenders can boost profitability by precisely estimating credit risk and efficiently managing their credit portfolios. These can include generating income from high quality credit interest payments and lowering default losses. Furthermore, through improved decisioning, clients that are onboarded are in line with the company’s risk tolerance. Understanding the client risk portfolio, such as having quotas for the number of high-risk customers, allows organisations to optimise the number of high risk/high reward accounts.
These models also enable greater transparency, which is critical for explainability. Having fixed and documented rules for credit acceptance, creates clear transparency for business decision making and means that all customers are treated equally. Decisions of customer rejections are also explainable, so if the rejected person/entity asks for feedback, the organisations can explain their reasoning for rejecting. As a result, there’s no ‘grey area’ around credit decisioning, and it’s no longer a ‘nice-to-have’, but a regulatory ‘must-have’ General Data Protection Regulation (GDPR) has provisions that require explainability for decisions made by automated systems that affect individuals. Article 13 requires that data controllers provide individuals with certain information about the processing of their personal data, including “the existence of automated decision-making, including profiling, and, at least in those cases, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.”
Whilst Article 22 provides additional requirements for automated decision-making, including the right for individuals to obtain “meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.” This means that if a decision is made solely by automated means and has legal or similar significant effects on the individual, the individual has the right to obtain an explanation of the decision-making process.
Similarly, the Financial Industry Regulatory Authority (FINRA) and the Securities and Exchange Commission (SEC) in the United States have issued guidance on the importance of explainability in algorithmic trading and other financial decision-making processes. There’s also a growing movement in the machine learning community towards developing explainable AI (XAI) techniques that can provide more insight into the inner workings of complex models. XAI techniques can help data scientists and stakeholders understand exactly how a model arrived at a particular decision or prediction, and can also help identify potential biases or other issues that may be affecting the model’s performance.
Improving the accuracy, transparency and profitability of credit decisions are themselves beneficial to the organisation in terms of mitigating risk, but ultimately, they also serve to enhance the customer experience too. CRMs analyse huge amounts of data to be able to provide instant decisions which means customers can be onboarded faster which is more convenient for the customer and reduces early life attrition.
Research from Technavio predicts significant growth in the global consumer credit market over the next few years, with a projected CAGR of 6.2% between 2022 and 2025. This growth is expected to be driven by factors such as increased consumer spending and rising demand for alternative funding options. As a result, CRM is going to become an increasingly important tool for many organisations, not just traditional financial institutions, moving forward.
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