Credit Risk Modeling & Machine Learning Use Cases

Have you ever wondered how banks and financial institutions decide who to lend money to, or how much to lend? The secret lies in credit risk modeling, a sophisticated approach that evaluates the likelihood of a borrower defaulting on their loan. Through in-depth analysis of historical data and borrower’s credit behavior, these models play a pivotal role in guiding lending decisions, managing risks, and ultimately, driving profitability. In the face of growing financial complexities, traditional methods are often insufficient.

That’s where machine learning comes into play that helps better anticipate credit risk. By automating the identification of patterns within data, patterns that often go unnoticed by human analysis, machine learning algorithms provide more nuanced and accurate predictions. These models not only learn from historical data but continually refine their predictions as new data streams in. This blog post will explore how machine learning is revolutionizing credit risk modeling in banking and financial institutions. If you’re a data scientist or an AI enthusiast, understanding these trends will be crucial for leveraging machine learning in your credit risk projects.

What is credit risk modeling and why is it important?

Credit risk is the risk that a borrower will not be able to repay their loan. This inability to repay can be triggered by a variety of circumstances. For instance, a borrower may lose their job and thus their primary income source, fall into a personal financial crisis due to unforeseen expenses or market changes, or even pass away, leaving the loan unpaid.

Credit risk modeling serves as a tool to quantify this risk, enabling a more systematic prediction of loan default probabilities. By leveraging historical data on borrowers’ credit behavior, these models can extrapolate patterns and trends that help estimate the likelihood of future defaults. This information is invaluable for financial institutions as it informs critical lending decisions.

Consider the case of a large multinational bank like HSBC or Citigroup. They handle millions of loan applications each year. With credit risk models, they can make data-driven decisions about who is worthy of a loan, the optimal loan amount, and appropriate interest rates. It also guides them on when to increase scrutiny on a loan or commence actions to mitigate potential losses.

On a more granular level, suppose a borrower has a high-risk profile, indicated by their credit history – numerous late payments, high credit utilization, or past defaults. The model would suggest a higher interest rate for this borrower or even decline the loan application, mitigating potential losses for the bank. In contrast, a borrower with a robust credit history and stable income would likely secure a loan at favorable interest rates. Thus, credit risk modeling is integral to prudent and profitable lending decisions.

Benefits of Credit Risk Modeling

The following represents some of the key reasons why one must go for Credit Risk Modeling:

• Who to lend: Credit risk modeling is important because it helps make better decisions about who to lend money to. This can help avoid lending to high-risk borrowers, who are more likely to default on their loans. It can help assess the creditworthiness of potential borrowers.
• How much to lend: Credit risk modeling helps banks and financial institutions to extend the right loan amount. By lending too much money to high-risk borrowers, a bank or financial institution can quickly become insolvent. Credit risk modeling can help avoid this by helping to extend loans in a more responsible manner.
• When to pull back: Credit risk modeling help banks and financial institutions to pull back when necessary. There may be times when a borrower’s circumstances change and they become a higher risk. Credit risk modeling can help to identify these changes early and take appropriate action.
• Automated decision making: Credit risk modeling helps to make automated decisions. Credit risk models can be used to automate decision-making processes. This can help to speed up the credit approval process and make it more efficient.
• Managing exposure to credit risk: Credit risk modeling can help banks and financial institutions manage their exposure to credit risk. This means that they can keep track of how much money they have lent out and how much it is at risk of being defaulted on. This information can help banks and financial institutions make more responsible decisions about lending money.

Strategies for managing credit risk

There are a number of different strategies that banks and financial institutions use for managing credit risk. Some of these include the following:

• Credit limit management: Banks and financial institutions use credit limit management to keep track of how much money they have lent out and how much it is at risk of being defaulted on. This information can help banks and financial institutions make more responsible decisions about lending money. Credit limit management can also help to identify when a borrower has become a high-risk and needs to be pulled back.
• Credit scoring: Credit scoring is a process of assigning a score to a borrower, which represents their creditworthiness. This score is used by banks and financial institutions to decide whether or not to lend money to them. Credit scoring can also help to identify high-risk borrowers early on, so that appropriate action can be taken.
• Credit monitoring: Credit monitoring is the process of tracking a borrower’s credit behavior over time. This can help banks and financial institutions identify any changes in their credit risk profile. Credit monitoring can also help to predict when a borrower might default on their loan.

How does machine learning help with credit risk modeling?

The following are some of the credit risk modeling use cases which can be dealt with machine learning:

• In credit risk modeling, determining who to lend money to is a crucial task that can be addressed as a classification problem using machine learning. Here, machine learning models, such as logistic regression, decision trees, random forest, support vector machines (SVM), and neural networks, are trained on historical data, encompassing both defaulters and non-defaulters. Algorithms like random forest, SVM, and neural networks are particularly useful as they can handle complex and non-linear relationships between the features and the target variable.  The aim is to discern patterns and behaviors that distinguish good borrowers from the bad.
  
  These models learn from several features or variables. The credit score, for instance, is a key factor. Banks consider a high credit score as an indicator of a customer’s financial discipline and lower likelihood to default. The loan amount is another important variable; excessive loans to a single customer can pose a risk if the customer is unable to repay. Credit history, too, carries significant weight as it reveals the customer’s past behavior with credit. Factors such as the number of timely repayments, the number of defaults or delinquencies, and the usage of available credit, are all crucial aspects of a customer’s credit history. The demographic and socio-economic information may also get considered as features while training the classification models. For instance, the model may use the employment history to evaluate the stability of the customer’s income. A borrower with a long, consistent employment record may be viewed as less risky compared to someone with a patchy employment history. Similarly, age can be a determinant of risk as it may give an indication of the loan repayment period.
• Determining how much money to lend to a particular customer is a crucial aspect of credit risk modeling that can be effectively addressed using regression models in machine learning. These models can predict continuous outcomes, such as the loan amount, based on a number of features or independent variables.
  
  The first step in this process involves feature selection. Features like credit score, credit history, debt to income ratio, credit utilization, and employment history, among others, are integral to predicting an appropriate loan amount. The credit score, for example, is a numerical expression based on an individual’s credit files, signifying the creditworthiness of the individual. A higher credit score implies a financially responsible behavior, leading to larger loan approvals. Credit history provides insights into the borrower’s past behavior with their credit. The absence of defaults or delinquencies in the credit history can influence the decision to offer a larger loan. Debt to income ratio represents the percentage of a customer’s monthly gross income that goes towards paying debts. A lower debt-to-income ratio suggests that the borrower has a good balance between debt and income, which could justify a higher loan amount. Credit utilization ratio, which is the amount of outstanding balances compared to the customer’s total available credit, is another key indicator. A low credit utilization rate reflects positively on the borrower’s credit profile and may lead to larger loans. Stable employment history typically indicates consistent income, making the borrower a less risky prospect, thus warranting a higher loan amount.
  

  Linear regression, Lasso regression, Ridge regression, and support vector regression are some of the machine learning algorithms used for this task. Linear regression, one of the simplest models, predicts the loan amount as a linear function of the selected features. However, it may not perform well if there are non-linear relationships or interactions between the features. On the other hand, Lasso and Ridge regression, which are extensions of linear regression, incorporate penalty terms in the cost function to avoid overfitting and manage multicollinearity between the features, respectively. Support Vector Regression, another powerful tool, can capture non-linear relationships using the kernel trick, offering robustness against outliers.

• Classification model to determine when to pull back: After a customer has been given a loan, it is important to track their credit behavior over time in order to identify any changes in their credit risk profile and help take the decision related to when to pull back.

Conclusion

Credit risk modeling is the process of predicting who to lend money to and how much to extend. Credit monitoring helps track a borrower’s credit behavior over time in order to identify any changes in their credit risk profile. Machine learning can help with both of these tasks by using predictive models that are trained on past data. These models can be used to predict whether a new customer is likely to default on their loan or not. Banks and financial institutions use machine learning for other purposes such as fraud detection and prevention. Machine learning can help banks make better decisions about who to lend money to, how much to extend, and when to pull back. Machine learning is also being used to improve fraud detection and prevent losses. In case you want to learn more, please drop us an inquiry!