摘要
随着商业银行业务数据的爆炸式增长，充分发挥数据价值的重要性也逐步突显，如何利
用好海量的数据资源成为商业银行可持续发展的关键。目前，商业银行通过贷款业务获得的
利润仍然占据利润总额的很大部分，所以商业银行能否与时俱进，充分挖掘数据资源，完善
信贷风险管理流程，增强信贷风险防控能力，有效降低贷款不良率，从而不断提高盈利水平，
是商业银行发展的重中之重。
本文在概述银行信贷风险管理相关理论和数据挖掘技术的基础上，首先介绍了 A农商银
行的基本情况和业务发展概况，着重分析了 A农商银行信贷风险管理现状及其存在的问题；
考虑到A农商银行信贷业务主要客户群体是个人信贷客户，本文选取A农商银行的个人信贷
数据构建信贷风险识别模型，具体包括：选取A农商银行26818条个人信贷业务数据，采用
缺失值处理、重复数据处理等多种方法预处理数据，基于决策树算法构建信贷风险识别模型，
并对模型进行评价验证了模型的有效性；最后，结合分析结果，本文探讨了 A农商银行的信
贷风险防控策略。
关键字：银行；信贷风险管理；数据挖掘；风险识别建模
I

Abstract
With the explosive growth of business data of commercial banks, the importance of giving full
play to the value of data is gradually highlighted. How to make good use of massive data resources
has become the key to the sustainable development of commercial banks. At present, the profit of
commercial banks through loan business still accounts for a large part of the total profit, so whether
commercial banks can keep pace with the times, improve the credit risk management process and
enhance the ability of credit risk prevention and control by fully mining data resources and effectively
reduce the non-performing loan rate, so as to continuously improve the level of profitability is the top
priority of the development of commercial banks.
On the basis of summarizing the theory of bank credit risk management and data mining
technology, this paper first introduces the basic situation and business development of A rural
commercial bank, and emphatically analyzes the current situation and existing problems of A rural
commercial bank's credit risk management. Considering that A rural commercial bank's main credit
business is personal credit, this paper selects A rural commercial bank's personal credit data to build
a credit risk prediction model, including: selecting 26818 personal credit business data of A rural
commercial bank, using missing value processing, repeated data processing and other methods to
preprocess the data, and building a credit risk prediction model based on decision tree algorithm. The
effectiveness of the model is verified by the evaluation of the model. Finally, combined with the
analysis results, this paper discusses the credit risk prevention and control strategy of A rural
commercial bank.
Key words：bank；credit risk management；data mining；risk identification modeling
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