Intelligent Assessment of Personal Credit Risk Based on Machine Learning

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