基于案例推理系统优化的个人信用评分研究
发布时间：2018-07-31 19:23
【摘要】：改革开放以来我国银行业迅速发展，个人信用评估在银行业的重要性日显突出。目前国内外的个人信用评分模型以统计学模型和人工智能方法为主。这些方法各有利弊，统计学方法可以提供假设检验但精度不高，人工智能方法精度较高但解释性不强。而且，这些成熟个人信用评分模型都面临着拒绝推论和信用样本动态变化的问题。目前大多评分机构仅用已获得贷款的部分客户作为样本训练模型来预测整体信用客户人群，这将导致非随机性样本偏差，直接影响评分模型的有效性；拒绝推论就是对这种样本偏差的纠正。信用样本动态变化指信用样本由于各种因素造成个人信用状态发生变化或信用人群由于经济社会的发展而发生整体漂移，，这会使得信用评价模型的结果和现实出现越来越大的偏差。拒绝推论和信用样本动态变化是个人信用评分领域中亟待解决的问题。 案例推理模拟人类大脑认知过程，具有很强的理论基础和广泛的应用背景，有望成为能够解决拒绝推论的动态信用评分模型。首先，本文根据案例推理的发展现状，构建了传统案例推理信用评分系统。通过该系统的应用发现案例推理在我国个人信用评估中既具有优越性同时也存在着局限性，这些局限性包括现有银行数据的影响和传统案例推理假设的制约。其次，针对这些现实制约因素，分别从案例库和案例推理循环两个层面对案例推理系统进行了优化。案例库的优化包括案例表达的优化、拒绝样本的引入和案例库的动态优化；推理循环的优化包括了神经网络与K最近邻法的混合案例检索方法和贝叶斯案例重用方法。最后，利用深圳某商业银行的个人信用数据对优化的案例推理系统进行了应用。结果表明，优化后的案例推理系统对拒绝样本的识别能力显著增强，能够很好地处理拒绝推论和信用动态变化问题；优化的案例推理系统较传统案例推理系统在准确性上有所提高，而且在稳定性和解释性上有了很大改进；优化的案例推理系统是一个能够和商业银行信用政策相互动态适应的个人信用评分方法，对银行的信贷政策具有政策支持性。
[Abstract]:Since the reform and opening up, China's banking industry has developed rapidly, and the importance of personal credit evaluation in the banking industry has become increasingly prominent. At present, the personal credit scoring models at home and abroad are mainly statistical models and artificial intelligence methods. These methods have their own advantages and disadvantages. Statistical methods can provide hypothetical test but the accuracy is not high, artificial intelligence method has higher precision but not strong explanation. Moreover, these mature personal credit scoring models are faced with the problems of rejection inference and dynamic changes of credit samples. At present, most rating organizations only use some clients who have received loans as sample training model to predict the whole credit customer population, which will lead to non-random sample deviation, which directly affects the effectiveness of the rating model. Rejection of inference is the correction of this sample bias. The dynamic change of credit sample refers to the change of individual credit status caused by various factors or the overall drift of credit population due to the development of economy and society. This will make the results of credit evaluation model and reality appear more and more big deviation. Rejection inference and dynamic change of credit samples are the problems to be solved in the field of personal credit scoring. Case-based reasoning (CBR), which simulates the cognitive process of human brain, has strong theoretical foundation and extensive application background, and it is expected to be a dynamic credit scoring model which can solve the problem of rejection inference. Firstly, according to the development of CBR, this paper constructs the traditional CBR credit scoring system. Through the application of this system, it is found that Case-Based reasoning (CBR) has both advantages and limitations in personal credit assessment in China. These limitations include the influence of existing bank data and the restriction of traditional Case-Based reasoning hypothesis. Secondly, the case-based reasoning system is optimized from two aspects: case base and case-based reasoning cycle. The optimization of case base includes the optimization of case representation, the introduction of rejected samples and the dynamic optimization of case base. The optimization of reasoning cycle includes the hybrid case retrieval method of neural network and K-nearest neighbor method and Bayesian case reuse method. Finally, the optimized case-based reasoning system is applied using the personal credit data of a commercial bank in Shenzhen. The results show that the optimized Case-Based reasoning (CBR) system can effectively deal with the problem of rejection inference and the dynamic change of credit. The optimized CBR system is more accurate than the traditional CBR system, and has great improvement in stability and explanation. The optimized Case-Based reasoning system (CBR) is a kind of personal credit scoring method which can dynamically adapt to the credit policy of commercial banks and has policy support to the credit policies of banks.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.479;F224
本文编号：2156588
[Abstract]:Since the reform and opening up, China's banking industry has developed rapidly, and the importance of personal credit evaluation in the banking industry has become increasingly prominent. At present, the personal credit scoring models at home and abroad are mainly statistical models and artificial intelligence methods. These methods have their own advantages and disadvantages. Statistical methods can provide hypothetical test but the accuracy is not high, artificial intelligence method has higher precision but not strong explanation. Moreover, these mature personal credit scoring models are faced with the problems of rejection inference and dynamic changes of credit samples. At present, most rating organizations only use some clients who have received loans as sample training model to predict the whole credit customer population, which will lead to non-random sample deviation, which directly affects the effectiveness of the rating model. Rejection of inference is the correction of this sample bias. The dynamic change of credit sample refers to the change of individual credit status caused by various factors or the overall drift of credit population due to the development of economy and society. This will make the results of credit evaluation model and reality appear more and more big deviation. Rejection inference and dynamic change of credit samples are the problems to be solved in the field of personal credit scoring. Case-based reasoning (CBR), which simulates the cognitive process of human brain, has strong theoretical foundation and extensive application background, and it is expected to be a dynamic credit scoring model which can solve the problem of rejection inference. Firstly, according to the development of CBR, this paper constructs the traditional CBR credit scoring system. Through the application of this system, it is found that Case-Based reasoning (CBR) has both advantages and limitations in personal credit assessment in China. These limitations include the influence of existing bank data and the restriction of traditional Case-Based reasoning hypothesis. Secondly, the case-based reasoning system is optimized from two aspects: case base and case-based reasoning cycle. The optimization of case base includes the optimization of case representation, the introduction of rejected samples and the dynamic optimization of case base. The optimization of reasoning cycle includes the hybrid case retrieval method of neural network and K-nearest neighbor method and Bayesian case reuse method. Finally, the optimized case-based reasoning system is applied using the personal credit data of a commercial bank in Shenzhen. The results show that the optimized Case-Based reasoning (CBR) system can effectively deal with the problem of rejection inference and the dynamic change of credit. The optimized CBR system is more accurate than the traditional CBR system, and has great improvement in stability and explanation. The optimized Case-Based reasoning system (CBR) is a kind of personal credit scoring method which can dynamically adapt to the credit policy of commercial banks and has policy support to the credit policies of banks.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.479;F224
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