An Application of Logistic Regression in Bank Lending Prediction: A Machine Learning Perspective

Abstract

Logistic regression has been widely applied in bank lending prediction due to its simplicity, efficiency, and interpretability. This paper provides a review of the theoretical foundations, practical applications, and recent advancements of logistic regression in this domain. Through case studies and literature analysis, the author focuses on the challenges faced by logistic regression in the modeling process, such as data imbalance and nonlinear relationships, as well as optimization strategies like regularization and ensemble learning. Despite the proliferation of machine learning methods, logistic regression remains the preferred choice and benchmark for credit risk modeling, thanks to its robust predictive performance and regulatory friendliness. Looking forward, the author argues that the integration of logistic regression with other machine learning techniques, coupled with domain knowledge, will further enhance its applicability and predictive power. However, striking a balance between model complexity and interpretability, while improving model robustness and extrapolation, remains an important area for future research. This paper offers new perspectives and insights for credit risk management practices in banks and other financial institutions.