Many credit scoring techniques have been used by banks to build credit scorecards. Among them, logistic regression model is the most commonly used in the banking industry. In the banking industry, logistic regression, linear regression, linear programming and classification tree have been used to develop credit scorecard systems. Logistic regression is the most commonly used one due to its distinctive features. This model coupled with random coefficients is proposed to improve the prediction accuracy of logistic regression without sacrificing its desirable features.
Importance of Logistic Regression in Credit Risk
Logistic regression can be used to predict default events and model the influence of different variables on a consumer’s creditworthiness. It can predict the creditworthiness of bank customers using predictors related to their personal status and even financial history. Model adequacy and robustness checks are done to ensure that the model is being properly fitted and interpreted.
Determining Target Variables via Logistic Regression
The dependent target variable is binary in nature when it comes to logistic regression. Say if we are focused on a single person’s creditworthiness, then it’s like a person can either default or not default on a loan. The same condition applies in the macro level as well. Whether a country is in a good condition, is eligible for loan or not.
To run any ML model, we would require a large amount of historical data. This data should contain both: the target and the thing we’re trying to predict, the features. These models are built to connect the feature data to the target data. After training the data, the model looks at cases where the feature data is known and the target data isn’t, and the model can make predictions. If somebody defaulted on a loan in the past, then we can say that there was a 0 probability that they paid back their loan. If somebody actually did pay back their loan, then the probability of repayment of their loan was 1.
Interpretation and Robustness Check of Logistic Regression
For the logistic regression model having logit link function, the coefficients for each variable can be interpreted in terms of multiplicative factors for the odds of a consumer’s creditworthiness, relative to the reference category. To find which variables explain creditworthiness and in what way, we test the significance of each group of variables. We do this by running a likelihood ratio test. Sometimes, there might be “bias” associated with the way loans are issued. Banks may be more stringent when it comes to loaning a consumer with bad credit history. Whereas, consumers with good credit history might not face the same kind of scrutiny and may end up being issued a loan they eventually cannot repay. It actually signifies a data issue in which the categories were incorrectly labeled.
Conclusion
Credit risk modeling is a field with access to a large amount of diverse data. ML models are built on this data and can be deployed to add analytical value. As ML is becoming more represented and influential in finance, it is important to recognize its benefits and drawbacks to prudently evaluate its performance. The logistic regression model can produce higher accuracy ratio, AUROC, and KS statistic values. The optimization function usually tends to include a regularization term (e.g., lasso, elastic net, or ridge) to limit the overfitting.
