【摘要】：小額信貸最初的目的是扶貧,后來(lái)服務(wù)的對(duì)象變得更加廣泛并逐漸轉(zhuǎn)向商業(yè)化。從20世紀(jì)70年代開(kāi)始,小額信貸從無(wú)到有,逐漸發(fā)展壯大。在經(jīng)濟(jì)發(fā)展中,小額信貸起到了重要作用。與此同時(shí),在小額信貸快速發(fā)展的背后也蘊(yùn)含了多種風(fēng)險(xiǎn),信用風(fēng)險(xiǎn)便是其中之一。信用風(fēng)險(xiǎn)是指借款人在到期日無(wú)法償還貸款或者不愿償還貸款而給放款人造成損失的風(fēng)險(xiǎn)。對(duì)小額貸款信用風(fēng)險(xiǎn)評(píng)估的準(zhǔn)確性高低關(guān)乎小額借貸行業(yè)發(fā)展的好壞。本文對(duì)小額信貸的概念進(jìn)行了詳細(xì)闡述,并對(duì)相關(guān)理論進(jìn)行了梳理。在對(duì)相關(guān)信用風(fēng)險(xiǎn)評(píng)估模型進(jìn)行比較后,最終選擇BP神經(jīng)網(wǎng)絡(luò)模型作為本文評(píng)估小額信貸信用風(fēng)險(xiǎn)的模型。BP神經(jīng)網(wǎng)絡(luò)模型有強(qiáng)大的學(xué)習(xí)和推理能力,能夠處理非線性關(guān)系,仿真能力強(qiáng),這些優(yōu)點(diǎn)正是本文評(píng)估小額信貸信用風(fēng)險(xiǎn)所需要的。在參考已有文獻(xiàn)的基礎(chǔ)上,本研究在信用風(fēng)險(xiǎn)評(píng)估指標(biāo)體系建設(shè)和BP神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)設(shè)計(jì)上進(jìn)行了創(chuàng)新。之后,利用本文獲取的數(shù)據(jù)建立BP神經(jīng)網(wǎng)絡(luò)模型,得到了較為理想的結(jié)果。并且,本研究所得到的BP神經(jīng)網(wǎng)絡(luò)模型得到了業(yè)界人士的認(rèn)可,可以為小額信貸信用風(fēng)險(xiǎn)評(píng)估提供參考。本文得出的結(jié)論是:(1)小額信貸最初是出于扶貧的目的而誕生,在后來(lái)的發(fā)展中逐漸由扶貧性轉(zhuǎn)向商業(yè)化;(2)信用風(fēng)險(xiǎn)是小額信貸的主要風(fēng)險(xiǎn)之一,降低信用風(fēng)險(xiǎn)可從減少信息不對(duì)稱(chēng)、建立違約懲罰機(jī)制和增強(qiáng)借款人風(fēng)險(xiǎn)控制能力入手;(3)在小額信貸信用風(fēng)險(xiǎn)評(píng)估中,BP神經(jīng)網(wǎng)絡(luò)模型有一些獨(dú)特的優(yōu)勢(shì);(4)本文通過(guò)對(duì)小額信貸信用風(fēng)險(xiǎn)評(píng)估做實(shí)證分析,發(fā)現(xiàn)本研究中所建立的模型對(duì)違約預(yù)測(cè)的正確率要高于對(duì)不違約所做的預(yù)測(cè)正確率。針對(duì)小額信貸信用風(fēng)險(xiǎn)評(píng)估問(wèn)題,本文提出的可行對(duì)策有:(1)完善征信體系建設(shè),降低信息不對(duì)稱(chēng);(2)建立小額信貸違約懲罰機(jī)制;(3)增強(qiáng)借款人信用意識(shí),提高其風(fēng)控能力;(4)完善小額信貸信用風(fēng)險(xiǎn)評(píng)估體系;(5)改進(jìn)BP神經(jīng)網(wǎng)絡(luò)模型。
[Abstract]:Microfinance was originally intended to help the poor, but later became more widespread and gradually commercialized. Since the 1970 s, microfinance has grown from scratch. In economic development, microfinance played an important role. At the same time, there are many risks behind the rapid development of microfinance, and credit risk is one of them. Credit risk refers to the risk that the borrower is unable to repay the loan on the maturity date or is unwilling to repay the loan, thus causing losses to the lender. The accuracy of credit risk assessment of small loans is related to the development of microfinance industry. In this paper, the concept of microfinance is described in detail, and related theories are combed. After comparing the relevant credit risk assessment models, the BP neural network model is chosen as the model to evaluate the microcredit credit risk. The BP neural network model has strong learning and reasoning ability, and can deal with the nonlinear relationship. The simulation ability is strong, these advantages are exactly what this article needs to evaluate the microcredit credit risk. On the basis of reference to the existing literatures, this study innovates in the construction of credit risk assessment index system and the design of BP neural network structure. Then, the BP neural network model is established by using the data obtained in this paper, and the ideal results are obtained. In addition, the BP neural network model obtained in this paper has been recognized by the industry, which can provide a reference for the credit risk assessment of micro-credit. The conclusions of this paper are as follows: (1) Microcredit was first born for the purpose of poverty alleviation and gradually changed from poverty alleviation to commercialization in later development; (2) Credit risk is one of the main risks of microcredit. Reducing credit risk can begin by reducing information asymmetry, Establishing default penalty mechanism and enhancing borrower's risk control ability; (3) BP neural network model has some unique advantages in microcredit credit risk assessment; (4) this paper makes empirical analysis on microfinance credit risk assessment. It is found that the prediction accuracy of the model is higher than that of non-default prediction. In view of the problem of credit risk assessment of micro-credit, the feasible countermeasures proposed in this paper are as follows: (1) to improve the construction of credit system and reduce the information asymmetry; (2) to establish a penalty mechanism for microcredit default; (3) to enhance the borrower's credit consciousness. Improve its ability of wind control; (4) improve the credit risk assessment system of microcredit; (5) improve the BP neural network model.
【學(xué)位授予單位】：云南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP183;F832.4

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