模糊逻辑在银行信用风险评价中的应用

M. Ozerova, Nicolay Stoletovs, I. Zhigalov
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引用次数: 0

摘要

银行体系是一个不断发展的体系。银行的信息环境越来越大,由于用户和银行产品的增长,处理的信息量也在不断增加。为了降低风险,银行对个人和法人的财务状况进行评估。这项工作的目的是开发模糊多连接模型,旨在预测接收银行产品的积极或消极决策的接收。决定是根据得分做出的。评分包括对完成由信用风险评估机构的承销商制定的某种问卷进行打分。根据获得的积分结果,系统自动做出批准或拒绝发放贷款的决定。不同的银行有不同的评分模型。研究目的:本文考虑使用模糊模型来决定银行发行一种实现“软计算”概念的银行产品。方法。模糊逻辑方法在信用评分中的应用并不新鲜,但由于将其集成到现有系统中成本高昂,因此在实践中并未得到广泛应用。每家银行在评分时都使用自己的客户财务可靠性指标。大多数银行的指标是相同的,但在决定发行不同的银行产品时,它们的数值是不同的。选取一家真实银行的标准评分方法数据作为初始数据。为了预测银行向客户发行银行产品的决策,应用了模糊模型,提出了生产规则,确定了隶属函数。该模型侧重于同时处理来自多个客户、不同银行和不同评分模型的传入数据。结果。建立了基于模糊推理规则的客户评级评估和接收银行产品决策预测的数学模型。所得结果将应用于面向企业客户提供银行产品的多银行网络系统中。
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Application of Fuzzy Logic to Assess Banks' Credit Risk
The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.
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