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This loan default prediction means that customers with financial difficulties will be flagged up sooner rather than later. So you can address issues before they arise – and before a customer defaults. This is proactive credit risk management.

Machine Learning algorithms in predicting which customers will default on their loans based on their financial information and historical data.

At present, researchers generally use machine learning methods to predict loan defaults, including Logistic Regression, Decision Trees, Random Forest, XGBoost, and other advanced techniques. The main advantages of Logistic Regression lie in its simple understanding, sturdy performance, and easy implementation [6].

Logistic Regression naturally outperforms Linear Regression in predicting the probability of loan default since its outcome contains a continuous range of grades between 0 and 1, which represents the likelihood of an event occurring [7].

To predict loan defaults, we applied logistic regression, which is a parametric machine learning algorithm that models the probability of falling into either one of the binary classes (client will default with his loan or have a clear payment status).

We were able to conclude that the probability of a loan default may be predicted by loan interest rates, loan amount, and borrower income, among other factors.

Predicting Loan Default is highly dependent on the demographics of the people, people with lower income are more likely to default on loans.

For credit risk analysis, let define a random variable D that takes values 1 and 0, where the value of 1 (D = 1) means the loan is default and 0 means the loan is not default. Then the probability of default is defined as the probability of success for the random variable D, that is θ=P(D=1).