Intelligent Assessment of Personal Credit Risk Based on Machine Learning

• DOI: 10.3390/systems13020112
• Publication Date: 2025-02-12T08:41:57Z
• Authors: Chuansheng Wang; Hang Yu
• Keywords: TA168; big data; personal credit risk; risk assessment; T1-995; Technology (General); risk factor identification; Systems engineering

In the 21st-century global economy, rapid growth of finance industry, particularly in personal credit, fuels economic and market prosperity. However, expansion credit business has brought explosive amount data, which puts forward higher requirements for risk management financial institutions. To solve this problem, paper constructs an intelligent evaluation model under background big data. Firstly, based on forest optimization feature selection algorithm, combined with initialization chi-square check, adaptive seeding, greedy search strategies, key factors are accurately identified from high-dimensional Then, XGBoost algorithm is used to evaluate level customers, traditional Sparrow Search Algorithm improved by using Tent chaotic mapping, sine cosine search, reverse learning, Cauchy mutation strategy improve performance parameters. Finally, Lending Club dataset empirical analysis, experiment shows that improves accuracy assessment enhances ability control.