Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods
https://doi.org/10.26794/2587-5671-2020-24-6-38-50
Abstract
Microfinance is a way to fight poverty, and therefore is of high social significance. The microfinance sector in Russia is progressing. However, the engagement of microfinance organizations in illegal financial transactions associated with fraud, illegal creditors, money laundering, significantly limits their potential and has negative impact on their development. The aim of the paper is to study the possibilities to automate detection of unscrupulous microfinance organizations based on machine learning methods in order to promptly identify and suppress illegal activities by regulatory authorities. The author cites common fraudulent schemes involving microfinance organizations, including a scheme for cashing out maternity capital, a fraudulent lending scheme against real estate. The author carried out a comparative analysis of the results obtained by classification methods — the logistic regression method, decision trees (algorithms of two-class decision forest, Adaboost), support vector machine (algorithm of two-class support vector machine), neural network methods (algorithm of two-class neural network), Bayesian networks (algorithm of two-class Bayes network). The two-class support vector machine provided the most accurate results. The author analysed the data on microfinance institutions published by the Bank of Russia, the MFOs themselves, and banki.ru. The author concludes that the research results can be of further use by the Bank of Russia and Rosfinmonitoring to automate detection of unscrupulous microfinance organizations.
Keywords
microfinance organizations,
financial monitoring,
machine learning methods,
classification algorithms
JEL: E58, G21, C53
About the Author
Yu. M. Beketnova
Financial University
Russian Federation
Competing Interests: not
Russian Federation
Yuliya M. Beketnova — Cand. Sci. (Eng.), Assoc. Prof., Faculty of Applied Mathematics and Information Technology.
Moscow
Competing Interests: not
References
1. Sorokin A. S., Shilov V. A. Multivariate statistical analysis of the structure of the microfinance market in Russia. Internet-zhurnal Naukovedenie. 2016;8(1):10. (In Russ.). DOI: 10.15862/10EVN 116
2. Makarova E. B. Features of microfinancing in Russia. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 3: Ekonomika. Ekologiya = Science Journal of VolSU. Global Economic System. 2017;19(4):80-86. (In Russ.). DOI: 10.15688/jvolsu3.2017.4.9
3. Balashev N. B., Barkinkhoeva M. Kh. Development trends of the microfinance market in the Russian Federation. Ekonomika i biznes: teoriya i praktika = Economy and Business: Theory and Practice. 2019;(10-1):27-31. (In Russ.). DOI: 10.24411/2411-0450-2019-11207
4. Tsvetkov V. A., Dudin M. N., Saifieva S. N. Problems and prospects for the development of microfinance organizations in the Russian Federation. Finansy: teoriya i praktika = Finance: Theory and Practice. 2019;23(3):96-111. (In Russ.). DOI: 10.26794/2587-5671-2019-23-3-96-111
5. Ordynskaya M. E., Silina T. A. Availability of microfinance services for small businesses (based on materials from the Republic of Adygea). Vestnik Adygeiskogo gosudarstvennogo universiteta. Seriya 5: Ekonomika = Bulletin of the Adyghe State University. Series: Economics. 2018;(3):213-224. (In Russ.).
6. Chernykh S. I. Microfinance organizations in the domestic financial and credit system: Problems of development. Vestnik Instituta ekonomiki Rossiiskoi akademii nauk = Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2017;(2):139-146. (In Russ.).
7. Ershova I. V., Tarasenko O. A. Small and medium-sized enterprises: Transformation of the Russian crediting and microfinancing system. Vestnik permskogo universiteta. Yuridicheskie nauki = Perm University Herald. Juridical Sciences. 2018;(39):99-124. (In Russ.). DOI: 10.17072/1995-4190-2018-39-99-124
8. Evlakhova Yu. S. Russian microfinance organizations: Dynamics of development and the problem of involvement in illegal financial transactions. Finansy i kredit = Finance and Credit. 2018;24(7):1637-1648. (In Russ.). DOI: 10.24891/fc.24.7.1637
9. Volkov S. E., Loskutov I. N. Fraud in microfinance sphere. Moscow: Russian Academy of Natural Sciences; 2016. 20 p. (In Russ.).
10. Pavlidis N. G., Tasoulis D. K., Adams N. M., Hand D. J. Adaptive consumer credit classification. Journal of the Operational Research Society. 2012;63(12):1645-1654. DOI: 10.1057/jors.2012.15
11. Yap B. W., Ong S. H., Husain N. H.M. Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications. 2011;38(10):13274-13283. DOI: 10.1016/j.eswa.2011.04.147
12. Khemais Z., Nesrine D., Mohamed M. Credit scoring and default risk prediction: A comparative study between discriminant analysis and logistic regression. International Journal of Economics and Finance. 2016;8(4):39. DOI: 10.5539/ijef.v8n4p39
13. Li Z., Tian Y., Li K., Yang W. Reject inference in credit scoring using support vector machines. SSRN Electronic Journal. 2016. DOI: 10.2139/ssrn.2740856
14. Louzada F., Anacleto-Junior O., Candolo C., Mazucheli J. Poly-bagging predictors for classification modelling for credit scoring. Expert Systems with Applications. 2011;38(10):12717-12720. DOI: 10.1016/j.eswa.2011.04.059
15. Vukovic S., Delibasic B., Uzelac A., Suknovic M. A case-based reasoning model that uses preference theory functions for credit scoring. Expert Systems with Applications. 2012;39(9):8389-8395. DOI: 10.1016/j.eswa.2012.01.181
16. Marques A. I., Garcia V., Sanchez J. S. Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications. 2012;39(12):10916-10922. DOI: 10.1016/j.eswa.2012.03.033
17. Akkoç S. An empirical comparison of conventional techniques, neural networks and the three-stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. European Journal of Operational Research. 2012;222(1):168-178. DOI: 10.1016/j.ejor.2012.04.009
18. Wu W.-W. Improving classification accuracy and causal knowledge for better credit decisions. International Journal of Neural Systems. 2011;21(4):297-309. DOI: 10.1142/S0129065711002845
19. Zhu H., Beling P. A., Overstreet G. A. A Bayesian framework for the combination of classifier outputs. Journal of the Operational Research Society. 2002;53(7):719-727. DOI: 10.1057/palgrave.jors.2601262
20. Eskindarov M. A., Solov’eva V.I., eds. Paradigms of the digital economy: Artificial intelligence technologies in finance and fintech. Moscow: Cogito-Center; 2019. 325 p. (In Russ.).
Review
For citations:
Beketnova Yu.M. Analysis of Possibilities to Automate Detection of Unscrupulous Microfinance Organizations based on Machine learning Methods. Finance: Theory and Practice. 2020;24(6):38-50. https://doi.org/10.26794/2587-5671-2020-24-6-38-50
Views:
878
Email this article 