基于CLPSO-CatBoost的贷款风险预测方法

Loan Risk Prediction Method Based on CLPSO-CatBoost

贷款风险分析是全球金融机构面临的共同考验.在大数据背景下,通过机器学习算法预防贷款风险具有现实意义.针对贷款数据不平衡、噪声大等特点,本文采用Boruta特征选择算法对贷款数据进行重要性筛选;提出通过综合学习粒子群算法(Comprehensive Learning Particle Swarm Optimization,CLPSO)优化CatBoost集成学习算法(CLPSO-CatBoost)的贷款风险预测方法,该算法改善了全局搜索能力、避免了陷入容易陷入局部最优的问题.CLPSO-CatBoost相较于传统信用评估模型具有更好的准确性,有实际应用价值.

Loan risk analysis is a common test faced by global financial institutions.In the context of big data,it is of practical significance to prevent loan risks through machine learning algorithms.Aiming at the imbalance in loan data and high noise,this study uses the Boruta feature selection algorithm to sort the importance of loan data.In addition,it proposes the CatBoost integrated learning algorithm based on Comprehensive Learning Particle Swarm Optimization(CLPSO-CatBoost)for loan risk prediction.This algorithm improves the global search and avoids the local optimum.Compared with the traditional credit evaluation models,CLPSO-CatBoost has high accuracy.