金融信用风险评价中的数据挖掘技术综述

Data mining techniques in financial credit risk assessment: A review

信用风险是金融风险管理中的一个热点问题,和国家的宏观经济形式以及国际发展态势有着密切的关联。互联网信贷等新兴商业模式的出现和发展给金融科技带来了巨大的影响,如何充分发挥金融数据的价值成为数据挖掘技术急需解决的问题。以数据技术为核心的金融信用风险评价可以构建更准确、覆盖面更广的金融信用风险模型,将成为传统信用评估体系的有力补充和发展趋势。本文分析了金融信用风险评价所面临的挑战,阐述了数据挖掘在数据选择、预处理、信用风险建模过程中的关键技术和应用,并对金融信用风险评价未来的研究方向提出了一些思路。

Credit risk is a hot issue in financial credit risk management,closely related to the state＇s macro-economy and international development. The emergence and development of new business models have brought ever-increasing impact on financial domain. Financial credit risk assessment based on data technique is able to build more accurate and universal credit risk models,and will become the powerful supplement and development tendency of traditional credit evaluation system in future. This paper analyzes the challenge to nowadays financial credit risk evaluation and illustrates the key data mining techniques in data preparation,preprocessing,and modeling when implementing a financial credit risk evaluation platform. Finally,the future research directions are discussed for financial credit risk assessment.