Development and Evaluation of the Effectiveness of Algorithms for an Automated Creditworthiness Assessment System for a Specific Segment of Microfinance Institution Clients

Citation: Diusheeva Aizhan, "Development and Evaluation of the Effectiveness of Algorithms for an Automated Creditworthiness Assessment System for a Specific Segment of Microfinance Institution Clients", Universal Library of Business and Economics, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Against the backdrop of the accelerated expansion of the global microfinance market and the parallel rise in credit risks, automating creditworthiness verification serves as a central catalyst for the resilience of microfinance institutions (MFIs). This study implements a comprehensive comparative-analytical approach to the development, performance, and implementation barriers of automated scoring algorithms in MFIs across markets with differing levels of maturity — in the United States, the European Union, and Kyrgyzstan. The objective is to explicate the key technological, regulatory, and socioeconomic determinants that define the applicability and effectiveness of machine learning (ML) models in these contexts. The methodological framework includes a systematic review of academic literature, a comparative analysis of industry reports, and a case study on the deployment of an automated system in MFIs in the city of Bishkek. The results indicate that ML algorithms — primarily XGBoost and random forest — deliver substantially higher predictive accuracy compared to traditional statistical approaches. At the same time, their performance is not universal and emerges as a function of alignment between model complexity and the quality of the local data ecosystem, the nature of the regulatory environment, and the level of organizational readiness of MFIs. In advanced jurisdictions, implementation is driven more by competitive pressure and the need for operational optimization, whereas in Kyrgyzstan it is driven by a mandate to expand financial access amid a shortage of valid data. The conclusion is that successful implementation requires context-adapted strategies; at the same time, the black box problem constitutes a fundamental challenge for MFIs with a pronounced social mission, increasing demand for explainable artificial intelligence (XAI) technologies. The material is intended for MFI executives, financial regulators, and researchers studying the impact of FinTech on financial inclusion.

Keywords: Microfinance, Credit Scoring, Machine Learning, Creditworthiness Assessment, Risk Management, Fintech, Comparative Analysis, Financial Access, Alternative Data, Kyrgyzstan.

https://doi.org/10.70315/uloap.ulbec.2025.0204003