梯度提升法在信贷风险评估中的应用研究  

Research on Application of Gradient Boosting Method in Credit Risk Assessment

作　　者：张哲滔 ZHANG Zhetao(University of North Carolina at Chapel Hill,North Carolina 27599,America)

机构地区：[1]北卡罗来纳大学教堂山分校,北卡罗来纳州27599

出　　处：《自动化应用》2024年第13期26-31,共6页Automation Application

摘　　要：开发了一种先进的机器学习模型,以通过预测贷款违约的可能性估计信贷风险。该模型利用不同的数据集,通过梯度提升方法评估年收入、信用记录和年龄等众多申请人因素,能提供稳健、稳定的预测结果,并能适应不断变化的消费者行为,大大提高了金融机构在贷款过程中作出明智决策的能力,最大限度地降低了金融风险,从而优化了风险管理策略。This paper develops an advanced machine learning model to estimate credit risk by predicting the likelihood of loan defaults.This model utilizes different datasets and evaluates numerous applicant factors such as annual income,credit history,and age through gradient boosting methods.It can provide robust and stable prediction results and adapt to constantly changing consumer behavior,greatly improving the ability of financial institutions to make wise decisions in the loan process,thereby minimizing financial risks and optimizing risk management strategies.

关 键 词：梯度提升法 信贷风险评估 贷款违约预测 机器学习 风险管理 

分 类 号：F830.5[经济管理—金融学]