基于BP神经网络的个人住房贷款还款信用评估
发布时间：2018-10-22 08:55
【摘要】：随着国家经济的不断发展,房地产业与人们的生活日益相关。不论是企业还是个人,都把投资房地产业作为资产投资的重要一项。至目前为止个人住房贷款已成为商业银行的最大业务之一,因此企业和个人的还款能力成为商业银行住房信贷风险管理中的一项重要参考指标。 在国外,商业银行在对客户还款能力评估方面已经进行很长时间的探索并取得很好的发展成果,目前商业银行基本采用统计的方法对客户还款能力进行量化分析。在我国,客户信用评估系统的建立相对落后,并未形成一个完整的体系,因而银行的信用风险较大。因此,加快商业银行信用评估系统建设的任务迫在眉睫,从而为商业银行提供科学的决策依据。 作为个人住房贷款还款信用评估的其中一种方法,神经网络模型以其自学习、自调整以及非线性映射的优点,被应用于量化个人信用评估模型中,但是神经网络在个人信用住房贷款评估中的实际应用并不是很广泛,有待进一步探索。在多种神经网络模型中,BP神经网络在神经网络中应用较为广泛,因为BP神经网络的具有自适应、较强泛化能力以及容错能力好的显著优点。本文利用某家商业银行客户住房贷款还款真实记录信息,并选取每一项还款记录中的11项评估指标构建评估体体系.然后在计算上利用MATLAB软件对数据进行仿真分析。同时,随着科研工作者对BP神经网络的研究不断深入,至目前为止已经产生了很多训练算法,对于这些训练算法性能优劣评比应该具体问题具体分析,从而针对某一具体问题找到其最最优算法。本文在BP神经网络模型的设计过程中,分别使用附加动量法、自适应学习速率、拟牛顿法、共轭梯度法、LM算法对BP神经网络模型进行训练,通过对比每个训练结果的误差、训练次数、训练时间及收敛速度,进而确定采用基于LM算法的BP神经网络。使用已经训练好的最优BP神经网络对重新选择的新客户的住房贷款信息进行测试,然后比较期望值与预测值之间的误差,考核该BP神经网络模型的泛化能力,试验结果表明该网络模型泛化能力良好。在基于LM算法的BP神经网络基础上,本文提出一种改进初始权值和样本数据随机性选取的方法,通过误判率的减小说明基于LM算法的改进BP神经网络的合理性及有效性,从而提供了个人住房信贷评价的可行解决方案。
[Abstract]:With the development of national economy, real estate industry is increasingly related to people's life. Whether enterprises or individuals, investment in real estate as an important asset investment. Up to now, personal housing loan has become one of the biggest business of commercial banks, so the repayment ability of enterprises and individuals has become an important reference index in housing credit risk management of commercial banks. In foreign countries, commercial banks have been exploring for a long time and have achieved good results in the evaluation of customer repayment ability. At present, commercial banks basically use statistical methods to quantify the repayment ability of customers. In our country, the establishment of customer credit evaluation system is relatively backward and does not form a complete system. Therefore, the task of speeding up the construction of credit evaluation system of commercial banks is urgent, thus providing scientific decision basis for commercial banks. As one of the methods to evaluate the repayment credit of personal housing loan, the neural network model is applied to the quantitative personal credit evaluation model because of its advantages of self-learning, self-adjustment and nonlinear mapping. However, the application of neural network in the evaluation of personal credit housing loan is not very extensive and needs further exploration. Among various neural network models, BP neural network is widely used in neural network, because BP neural network has the advantages of self-adaptation, strong generalization ability and good fault-tolerant ability. This paper uses the real record information of a commercial bank customer's housing loan repayment, and selects 11 evaluation indexes of each repayment record to construct the evaluation body system. Then MATLAB software is used to simulate and analyze the data. At the same time, with the further research of BP neural network, a lot of training algorithms have been produced so far. The evaluation of the performance of these training algorithms should be analyzed concretely. In order to find its optimal algorithm for a specific problem. In the course of designing the BP neural network model, we use the additional momentum method, adaptive learning rate, quasi-Newton method, conjugate gradient method and LM algorithm to train the BP neural network model, and compare the errors of each training result. The training times, training time and convergence rate are used to determine the BP neural network based on LM algorithm. Using the trained optimal BP neural network to test the housing loan information of the re-selected new customer, then compare the error between the expected value and the predicted value, and evaluate the generalization ability of the BP neural network model. The experimental results show that the generalization ability of the network model is good. On the basis of BP neural network based on LM algorithm, this paper proposes a method to improve the random selection of initial weight and sample data. The rationality and effectiveness of the improved BP neural network based on LM algorithm are illustrated by the reduction of misjudgment rate. Thus provides the individual housing credit appraisal feasible solution.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F832.45;TP183
本文编号：2286702
[Abstract]:With the development of national economy, real estate industry is increasingly related to people's life. Whether enterprises or individuals, investment in real estate as an important asset investment. Up to now, personal housing loan has become one of the biggest business of commercial banks, so the repayment ability of enterprises and individuals has become an important reference index in housing credit risk management of commercial banks. In foreign countries, commercial banks have been exploring for a long time and have achieved good results in the evaluation of customer repayment ability. At present, commercial banks basically use statistical methods to quantify the repayment ability of customers. In our country, the establishment of customer credit evaluation system is relatively backward and does not form a complete system. Therefore, the task of speeding up the construction of credit evaluation system of commercial banks is urgent, thus providing scientific decision basis for commercial banks. As one of the methods to evaluate the repayment credit of personal housing loan, the neural network model is applied to the quantitative personal credit evaluation model because of its advantages of self-learning, self-adjustment and nonlinear mapping. However, the application of neural network in the evaluation of personal credit housing loan is not very extensive and needs further exploration. Among various neural network models, BP neural network is widely used in neural network, because BP neural network has the advantages of self-adaptation, strong generalization ability and good fault-tolerant ability. This paper uses the real record information of a commercial bank customer's housing loan repayment, and selects 11 evaluation indexes of each repayment record to construct the evaluation body system. Then MATLAB software is used to simulate and analyze the data. At the same time, with the further research of BP neural network, a lot of training algorithms have been produced so far. The evaluation of the performance of these training algorithms should be analyzed concretely. In order to find its optimal algorithm for a specific problem. In the course of designing the BP neural network model, we use the additional momentum method, adaptive learning rate, quasi-Newton method, conjugate gradient method and LM algorithm to train the BP neural network model, and compare the errors of each training result. The training times, training time and convergence rate are used to determine the BP neural network based on LM algorithm. Using the trained optimal BP neural network to test the housing loan information of the re-selected new customer, then compare the error between the expected value and the predicted value, and evaluate the generalization ability of the BP neural network model. The experimental results show that the generalization ability of the network model is good. On the basis of BP neural network based on LM algorithm, this paper proposes a method to improve the random selection of initial weight and sample data. The rationality and effectiveness of the improved BP neural network based on LM algorithm are illustrated by the reduction of misjudgment rate. Thus provides the individual housing credit appraisal feasible solution.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F832.45;TP183
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