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Russian Market of Online Microloans to the Population: Credit Risks Analysis

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  • Yuliya S. Evlakhova
  • Alexandra A. Tregubova

Abstract

The credit bubble and the accompanying credit risks in the microfinance market could potentially threaten financial stability, as a crisis in the microfinance market could trigger an influx of high-risk clients into the banking sector. The purpose of the study is to assess the level of credit risk that creates threats of financial instability in the microloan market for the population, including its online segment. The hypothesis of the study is that during periods of crisis there are critical deviations of the key characteristics of the credit risk of the microloan market for the population, including its online segment. An original approach to assessing credit risk in the market of microloans to the population is proposed, which includes: (1) selection of indicators of the microfinance market that characterize credit risk at the macrolevel; (2) exponential smoothing models that make it possible to obtain an estimate of the boundaries of the corridor of acceptable values of market indicators. The information base of the study was the official statistical data of the Bank of Russia on the activities of microfinance institutions. The results of the study made it possible to identify the emergence of several "bubbles" of online microloans in the microfinance market: the largest of them was recorded on March 31, 2021, and was due to an increase in the issuance of both online PDL and online IL microloans, the second largest was recorded on June 30, 2022, and was caused exclusively by the growth of online PDL microloans. It was revealed that the level of credit activity of microfinance organizations shifted from high to moderate, while their credit activity did not provoke an excessive increase in credit risks of the microfinance market. In order to timely prevent crises in the microfinance market, the necessity of monitoring the values of outstanding overdue debts of microfinance organizations and interest arrears on microloans issued online IL is substantiated. The practical significance of the study lies in the possibility of using the results obtained in the prudential regulation of the microfinance market, in assessing the risks of the activities of microfinance organizations and their clients – individual borrowers. The theoretical significance of the study lies in the expanding of the analysis of credit risks in the microfinance market through the study of the segment of online microloans.

Suggested Citation

  • Yuliya S. Evlakhova & Alexandra A. Tregubova, 2023. "Russian Market of Online Microloans to the Population: Credit Risks Analysis," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 710-738.
  • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:3:p:710-738
    DOI: https://doi.org/10.15826/vestnik.2023.22.3.029
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    References listed on IDEAS

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    1. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    2. Sydney Chikalipah, 2018. "Credit risk in microfinance industry: Evidence from sub-Saharan Africa," Review of Development Finance Journal, Chartered Institute of Development Finance, vol. 8(1), pages 38-48.
    3. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    4. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    5. Hartoyo & Fermico Karambut & Rita Nurmalina & Mukhamad Najib, 2019. "The Intention in Online Submission of Micro Credit," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 186-200.
    6. Daniel Kandie & Khan Jahirul Islam, 2022. "A new era of microfinance: The digital microcredit and its impact on poverty," Journal of International Development, John Wiley & Sons, Ltd., vol. 34(3), pages 469-492, April.
    7. Nitin Navin & Pankaj Sinha, 2019. "Market Structure and Competition in the Indian Microfinance Sector," Vikalpa: The Journal for Decision Makers, , vol. 44(4), pages 167-181, December.
    8. Lawrence, Michael J. & Edmundson, Robert H. & O'Connor, Marcus J., 1985. "An examination of the accuracy of judgmental extrapolation of time series," International Journal of Forecasting, Elsevier, vol. 1(1), pages 25-35.
    9. Wang, Bo & Yu, Yunjun & Yang, Ziying & Zhang, Xiaomei, 2021. "Microfinance institutions and Peer-to-Peer lending: What does microfinance competition bring?," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    microfinance market; online microloans; risk; credit risk; exponential smoothing; risk assessment; financial instability.;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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