A Comparative Study of Machine Learning Algorithms in the Prediction of Personal Loan Defaults

Abstract

This article focuses on the comparative study of the application of machine learning algorithms in the field of personal loan default prediction. This paper articulates the critical role of predicting personal loan defaults and systematically dissects the mathematical principles and derivation logic of mainstream machine learning algorithms. It conducts a rigorous comparative analysis focusing on their theoretical foundations, optimization objectives, and distinct characteristics in handling financial data. This paper explores the considerations of algorithms in the practical application of personal loan default prediction, including data quality, model interpretability, and business demand matching degree, etc. Analyze the challenges currently faced by research and practice, such as data privacy and security, algorithmic bias, etc., and propose corresponding countermeasures. It aims to provide theoretical references for financial institutions to rationally select and apply machine learning algorithms in the prediction of personal loan defaults, and promote the improvement of financial risk management levels.