Weighted Assessment of the Microcredit Borrower Solvency Using a Fuzzy Analysis of Personal Data

Aliyev, Elchin; Gaziyev, Zaur

doi:10.1007/978-3-030-64058-3_66

Cited by 3 publications

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“…В [1][2][3][4] предлагаются различные нечеткие подходы к оценке кредитоспособности физических лиц, основанные на применении системы нечеткого вывода относительно уровня его кредитоспособности. В [5,6] предложено решение задачи оценки потенциальных заемщиков микрокредитов с применением нечеткого метода взвешенной максиминной свертки качественных критериев оценки кредитоспособности, которое предусматривает фаззификацию анкетных данных заявителей, т.е. представление их в виде нечетких множеств.…”

Section: Introductionunclassified

Аналитическая Поддержка Процесса Микрокредитования С Применением Системы Нечеткого Вывода.

Aliyev¹,

Aliyev²,

Ali³

2021

PC&I

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A comprehensive analysis of the applicantʼs solvency for obtaining a microcredit precedes the conclusion of a loan agreement with him. This allows to determine the risk factors associated with the possibility of non-repayment of a bank loan in due time, and, on the contrary, to assess the likelihood of timely repayment of the loan. Therefore, the assessment of the clientʼs creditworthiness is an integral part of the work of commercial banks and microfinance organizations to determine the possibility of issuing microloans to one or another applicant. The paper proposes a balanced approach to the multi-criteria assessment of the solvency of individuals, based, among other things, on a fuzzy analysis of their solvency indicators. The developed fuzzy inference system in combination with statistical methods for assessing solvency, can serve as an analytical core for a credit decision support system. Based on the example of ten hypothetical alternative borrowers, characterized by their current indicators, the corresponding assessments of their solvency were made, including scoring, Pareto method, Bord method and using a fuzzy inference system. Such a combined approach is distinguished by the ability to identify reliably a group of individuals with high credit discipline and the characteristics of those in relation to whom credit decisions are classified as high-risk.

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Section: Introductionunclassified