Optimizing Credit Risk Classification Using Machine Learning Techniques

Music City Center 

This study presents a systematic machine learning (ML) framework to classify loan applicants into creditworthy and non-creditworthy categories. The proposed methodology encompasses a comprehensive data preprocessing pipeline, including the imputation of missing values and feature selection using the Random Forest algorithm to identify key predictive variables. To address potential multicollinearity issues, a variance inflation factor (VIF) analysis is conducted to ensure model robustness and interpretability. Additionally, to mitigate class imbalance within the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enhancing the representativeness of the training data. Three ML models are subsequently trained and rigorously evaluated based on performance metrics. The results demonstrate the efficacy of the proposed approach in improving credit risk classification, providing financial institutions with a data-driven framework to enhance decision-making processes, optimize resource allocation, and support strategic lending initiatives. These findings underscore the transformative potential of predictive analytics in advancing financial risk management practices.

Machine Learning (ML)

Creditworthy

Classification

Credit Risk

Financial Institutions

Financial Risk Management 

Section on Risk Analysis