微型电脑应用

由于近年来我国经济的急速飞腾,银行业务蓬勃发展。信贷业务是银行的主流业务之一,但是如何评价借款人的信用风险已经成为当今互联网金融行业的热门课题之一,日益受到人们的注意。自2013年我国征信系统启动以来,信贷业务的主动风险控制一直是我国金融领域研究的热门话题之一。其本质为将客户准确的划分为信誉客户以及非信誉客户。而随着当代计算机业务的迅猛发展,机器学习算法逐渐在金融领域得到普及以及应用,结合机器学习算法中的GBDT算法,利用银行客户的基本信息、流水记录、用户检测信息以及用户检测量表等相关数据,进行综合评定。最后结合算法给出实例进行相关分析。

Due to the rapid development of the domestic financial industry in recent years,the banking business is booming.Credit business is one of the bank's mainstream businesses,but how to evaluate the borrower's credit risk has become one of the hot topics in the Internet finance industry today,and is increasingly attracting the attention of the contemporary.Since China's credit system was launched in 2013,active risk control of credit business has been one of the hot topics in the field of financial research in China.Its essence is to accurately divide customers into credit customers and non-credit customers.With the rapid development of the contemporary computer business,machine learning algorithms have been gaining popularity and application in the financial field.Therefore,this paper combines the basic information,flow records,user detection information,and user detection amount of the bank's customer with the GBDT algorithm in the machine learning algorithm to give a comprehensive assessment.Finally,combined with the algorithm,several examples are given for the correlation analysis.