Enhancing Credit Risk Assessment Through Machine Learning: A Behavioral Scoring Approach

Music City Center 

Artificial intelligence and machine learning advancements have transformed decision-making landscapes in the financial industry. With the advent of more complex credit products, the need for robust and innovative credit risk management is more critical.
We harness behavioral scoring insights to develop a machine learning model for credit risk management, thus providing deeper borrower profiling. Data collection was done through focus group interviews and secondary sources. Behavioral data were analyzed to identify patterns, while financial data underwent preprocessing and feature engineering to ensure compatibility with machine learning algorithms.
Machine learning models, including logistic regression, support vector machines, K-nearest neighbors, decision trees, extreme gradient boosting, light gradient boosting, and CatBoost, were trained and evaluated for accuracy, precision, recall, and F1-score. The results demonstrated the effectiveness of ensemble methods, particularly CatBoost, which outperformed other models with an accuracy of 0.87, a precision of 0.88, a recall of 0.86, and an F1-score of 0.87.

Behavioral scoring

Machine Learning

Credit Risk Assessment

CatBoost

Borrower profiling

Ensemble methods 

Section on Statistical Learning and Data Science