【作者】 庞立艳；

【导师】 徐永仁；

【作者基本信息】 哈尔滨工业大学 ， 管理科学与工程， 2008， 硕士

【摘要】 东南亚金融危机的爆发使得全球金融市场之间紧密联系、不同市场之间的互相影响、银行风险和金融危机在国际间的传播问题开始引起越来越多的关注。目前,许多金融机构陷入经营困境的主要原因不再是信用风险或市场风险等单一风险,而是由信用风险、市场风险外加操作风险相互交织、共同作用造成的。巴塞尔委员会于1998年推出《有效银行监管的核心原则》,至此,市场风险与信用风险、操作风险一并成为银行监管部门重点关注的对象。2005年11月,巴塞尔委员推出《新巴塞尔资本协议》,新资本协议对银行信用风险管理提出了更高的要求,对数据和量化方法提出了更高的要求。虽然我国商业银行对所面临的市场风险的认识程度有了一定的提高,同时监管部门的监管水平也有了较大程度的提高。但对商业银行所面临的信用、操作和市场这三大主要风险的研究和实践在我国尚处于摸索阶段。个人信用是市场经济的基础;没有个人信用,市场经济便失去了坚实基础。本文以银行贷款的信用风险为核心,主要研究了不同的数据挖掘方法在商业银行贷款信用风险评估中的性能,包括神经网络算法、遗传算法和决策树算法。首先,我们分别用这三种算法构建信用风险评估模型,然后通过模型对贷款数据进行分析,得出信用风险评估结果。最后,对三种模型的测试结果进行比较分析,研究不同的算法在信用风险评估中的性能,得出本文的结论,为银行贷款人员进行贷款信用风险分析提供决策依据。更多还原

【Abstract】 Since the 1997 Southeast Asian financial crisis, people have paid more attention to the close contact of global financial markets, mutual influence among different market, banking risks and financial crisis. At present, the main reason why many financial institutions get bogged down into operating difficulties is no longer caused by single risk such as market risk, credit risk, but by the plus intertwined, common function of credit risk, market risk and operational risk.Basel Committee launched "the core principles of effective banking supervision" in 1998, at this point, market risk and credit risk, operational risk become the object on which banking supervision departments focus. November 2005, the Basel Committee launched "New Basel Capital Accord". New Capital Accord puts higher requirements on bank credit risk management and data and quantitative approach. Although Chinese commercial banks have raised the awareness about market risks and the supervision levels supervision departments have also improved, the research and practice on credit, operational and market risk is still in the exploratory stage. Personal credit is the basis of market economy; without personal credit, the market economy will lose solid foundation.This paper, with bank loan credit risk as the core, mainly studies the performance of different methods of data mining in commercial bank loan credit risk assessment, including neural network algorithm, genetic algorithms and decision tree algorithm. First of all, we use the three algorithms to construct credit risk assessment model separately, and then analyze the loan data with the models we have constructed and get the results of credit risk assessment. Finally, we compare and analyze the test results of the three models, then study the strengths and weaknesses of different algorithm in credit risk assessment and come to the conclusion.更多还原