Use Credit Risk Models and Artificial Intelligence in Credit Concession

Credit Risk Models

Analytical models that automatically sanction credit approvals do not typically use AI as a basic part of the evaluation. This confirms that the optimal solution is not always the most complex. These models face challenges such as regulations, interpretability by different stakeholders (including regulators), and the calculation of the probability of default.

From calculating capital reserves to evaluating marketing strategies, statistical analysis influences almost every decision in a bank. Many individuals, including data scientists, analysts, managers, business leaders, and regulators, frequently consult analysis or reports based on this data. Machine learning and predictions from statistical models can be an part of this analysis, although they often do not play a primary role.

When a loan applicant approaches a bank, they undergo a credit risk assessment. This involves calculating the expected loss in case of default or the probability of default, which is the likelihood that the applicant will be unable to repay the loan due to financial difficulties. This latter prediction often drives the decision to grant a loan.

Banks have always evaluated credit risk using a "credit scoring card." This card considers various characteristics of a potential borrower, assigning scores based on different groups and summing them to obtain a final score. The higher the score, the lower the probability of default.

In the past, these cards were mainly created through "expert judgment." Today, banks have access to vast amounts of data and computing power to create statistically optimal credit scoring cards, resulting in fairer outcomes. Internally, a bank has access to its entire transaction history and debt behavior. Externally, most countries have public records of past debts and defaults. Some banks may even use data from devices, such as geolocation, though this can encounter regulatory and ethical considerations.

The process of creating these cards has changed, but the final product remains the same. Model characteristics are discretized using Weight of Evidence (WOE) to maximize the difference in default rates for each characteristic group, assigning scores that evaluate the probability of default.

How Credit Scoring Cards Are Used

These cards form the basis of a modern AI system used to automatically approve loans. In its simplest form, it is a strict cutoff: if the score is below the limit, the loan is denied. Additional rules may be based on the bank's strategy, regulations, or other characteristics such as the customer's age. More complex systems may use predictions from multiple models.

In practice, these systems are used to approve personal loans or loans to small businesses. Some cases, such as mortgages or business loans, are not fully automated due to their uniqueness or size, requiring a final human decision, often with the aid of a credit scoring card.

Surprisingly, despite advancements in data collection and automation, the underlying models are simple logistic regressions, which are sufficiently accurate and understandable.

Fraud Detection and other angles of the customer management

There are other aspects of the risk customer process, such as fraud detection, where a more diverse set of data sources is examined, such as transactional data, device data, or communication history. In these cases, linear models often do not meet the challenge, and more advanced AI models, such as Random Forests, XGBoost, or neural networks, are necessary. These and other related aspects will be covered in our next blog.

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