Classification of borrowers to improve the scoring system of microfinance organizations

Abstract

The development of information technology is accompanied by a comprehensive transformation of the service sector, including microcredit. This sector of the Russian financial market shows steady growth annually. However, amid the high debt load on the Russian population, the availability of microcredit for most citizens, including online, has led to a high share of default disbursements of microloans in MFIs. Pressure from the regulator and a decrease in the income of Russians led the majority of MFIs to bankruptcy, while the remaining players in the microfinance market led to lower interest rates, and as a result, their margins decreased significantly. In this regard, MFIs have an urgent need to develop a scoring model that would be able to identify high-margin borrowers at the stage of applying for a microloan and "cut off"potentially defaulted borrowers. As part of this work, a methodology is proposed for clustering borrowers based on the fuzzy criterion "level of financial responsibility"and the proposed classification of borrowers.