Modeling the Probability of Credit Default of Clients of Microfinance Organizations: The Case of One MFI

HSE Economic Journal Pub Date : 2019-01-01 DOI:10.17323/1813-8691-2019-23-4-497-523

K. Polyakov, Liudmila Zhukova

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引用次数: 0

Abstract

Microfinance organizations have become widespread in the crisis years, issuing microloans (up to 100000 rubles) at high interest rates almost without documents. Today, the Central Bank of Russia actively regulates this market, more and more tightening requirements, limiting rates and pennies on loans. This necessitates the formation of a new strategy for assessing the risk of non-repayment of a loan or loan, based on the prevention of delinquency on the part of customers. To do this, first, it is necessary to obtain more informative data about customers, without complicating the relationship with them. Secondly, it is necessary to have a good understanding of the possibilities of certain methods of classification in solving various problems of evaluating potential customers. The authors of this study analyze the importance for the clients classification quality of those indicators that are traditionally collected by MFIs, as well as the importance of some new indicators based on data from social networks. In this case, the importance of indicators is interpreted in the context of specific classification algorithms (methods).To model credit default (delay of more than 30 days), the authors use several algorithms for constructing classification trees – CART and C 4.5 algorithms, logistic regression and Random Forest algorithm. Modeling is carried out based on a sample of customer profiles of real MFI. Ambiguous results were obtained. Depending on the formulation of the problem of classification of customers have advantage different algorithms descriptive analysis (CART, C4.5, Logit). At the same time, as you might expect, the non-interpreted predictive algorithm “Random Forest” provides the best quality of forecasts. According to the results of the analysis, it was revealed that the credit history of the borrower, as well as his age, is of great importance for the classification of MFI clients. Gender had no impact on the classification results. In addition, data from social networks turned out to be unimportant.

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小额信贷机构客户信用违约概率建模:以一家小额信贷机构为例

小额信贷组织在危机年代变得普遍，几乎没有文件就以高利率发放小额贷款(最高10万卢布)。今天，俄罗斯中央银行积极监管这个市场，越来越严格的要求，限制利率和贷款利率。这就需要在防止客户违约的基础上，制定一项评估不偿还贷款或贷款风险的新战略。要做到这一点，首先，有必要在不使与客户的关系复杂化的情况下获得有关客户的更多信息数据。其次，有必要很好地了解某些分类方法在解决评估潜在客户的各种问题中的可能性。本研究的作者分析了传统上由小额信贷机构收集的指标对客户分类质量的重要性，以及基于社交网络数据的一些新指标的重要性。在这种情况下，指标的重要性是在具体的分类算法(方法)的背景下解释的。为了对信用违约(延迟超过30天)进行建模，作者使用了几种算法来构建分类树——CART和c4.5算法、逻辑回归和随机森林算法。以实际小额信贷机构的客户资料为样本进行建模。得到了模棱两可的结果。根据问题的制定对客户分类有不同优势的描述性分析算法(CART, C4.5, Logit)。与此同时，正如你所期望的那样，非解释预测算法“随机森林”提供了最好的预测质量。根据分析结果，借款人的信用记录以及年龄对于小额信贷机构客户的分类非常重要。性别对分类结果无影响。此外，来自社交网络的数据被证明是不重要的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

HSE Economic Journal Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： The HSE Economic Journal publishes refereed papers both in Russian and English. It has perceived better understanding of the market economy, the Russian one in particular, since being established in 1997. It disseminated new and diverse ideas on economic theory and practice, economic modeling, applied mathematical and statistical methods. Its Editorial Board and Council consist of prominent Russian and foreign researchers whose activity has fostered integration of the world scientific community. The target audience comprises researches, university professors and graduate students. Submitted papers should match JEL classification and can cover country specific or international economic issues, in various areas, such as micro- and macroeconomics, econometrics, economic policy, labor markets, social policy. Apart from supporting high quality economic research and academic discussion the Editorial Board sees its mission in searching for the new authors with original ideas. The journal follows international reviewing practices – at present submitted papers are subject to single blind review of two reviewers. The journal stands for meeting the highest standards of publication ethics.