基于BP神經網絡的個人住房貸款還款信用評估
發(fā)布時間：2018-10-22 08:55
【摘要】：隨著國家經濟的不斷發(fā)展,房地產業(yè)與人們的生活日益相關。不論是企業(yè)還是個人,都把投資房地產業(yè)作為資產投資的重要一項。至目前為止個人住房貸款已成為商業(yè)銀行的最大業(yè)務之一,因此企業(yè)和個人的還款能力成為商業(yè)銀行住房信貸風險管理中的一項重要參考指標。 在國外,商業(yè)銀行在對客戶還款能力評估方面已經進行很長時間的探索并取得很好的發(fā)展成果,目前商業(yè)銀行基本采用統計的方法對客戶還款能力進行量化分析。在我國,客戶信用評估系統的建立相對落后,并未形成一個完整的體系,因而銀行的信用風險較大。因此,加快商業(yè)銀行信用評估系統建設的任務迫在眉睫,從而為商業(yè)銀行提供科學的決策依據。 作為個人住房貸款還款信用評估的其中一種方法,神經網絡模型以其自學習、自調整以及非線性映射的優(yōu)點,被應用于量化個人信用評估模型中,但是神經網絡在個人信用住房貸款評估中的實際應用并不是很廣泛,有待進一步探索。在多種神經網絡模型中,BP神經網絡在神經網絡中應用較為廣泛,因為BP神經網絡的具有自適應、較強泛化能力以及容錯能力好的顯著優(yōu)點。本文利用某家商業(yè)銀行客戶住房貸款還款真實記錄信息,并選取每一項還款記錄中的11項評估指標構建評估體體系.然后在計算上利用MATLAB軟件對數據進行仿真分析。同時,隨著科研工作者對BP神經網絡的研究不斷深入,至目前為止已經產生了很多訓練算法,對于這些訓練算法性能優(yōu)劣評比應該具體問題具體分析,從而針對某一具體問題找到其最最優(yōu)算法。本文在BP神經網絡模型的設計過程中,分別使用附加動量法、自適應學習速率、擬牛頓法、共軛梯度法、LM算法對BP神經網絡模型進行訓練,通過對比每個訓練結果的誤差、訓練次數、訓練時間及收斂速度,進而確定采用基于LM算法的BP神經網絡。使用已經訓練好的最優(yōu)BP神經網絡對重新選擇的新客戶的住房貸款信息進行測試,然后比較期望值與預測值之間的誤差,考核該BP神經網絡模型的泛化能力,試驗結果表明該網絡模型泛化能力良好。在基于LM算法的BP神經網絡基礎上,本文提出一種改進初始權值和樣本數據隨機性選取的方法,通過誤判率的減小說明基于LM算法的改進BP神經網絡的合理性及有效性,從而提供了個人住房信貸評價的可行解決方案。
[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.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F832.45;TP183
本文編號：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.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F832.45;TP183
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