KnE Social Sciences

Published date: Feb 15 2018

Journal Title: KnE Social Sciences

Issue title: III Network AML/CFT Institute International Scientific and Research Conference "FinTech and RegTech"

Pages: 153-161

DOI: 10.18502/kss.v3i2.1537

A.V. Baryshevabarysheva.anna.v@gmail.comNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe shosse 31, Moscow, 115409

D.V. Domashovajanedom@mail.ruNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe shosse 31, Moscow, 115409

Е.Е. PisarchikEEpisarchik@mephi.ruNational Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe shosse 31, Moscow, 115409

The paper considers credit organizations as the pivotal elements of the state's economic and financial system. Credit institutions license withdrawal probability is estimated on the basis of binary choice models. A methodology for processing and analyzing credit institutions data based on regression analysis and multi-criteria optimization methods has been developed and used to identify bank groups potentially threatening the stability of the Russian banking system and the integrity of anti-money laundering and terrorist financing system (AML/CFT).

Keywords: credit institution license withdrawal, binary choice model, anti-money laundering and terrorist financing.

[1] Liquidation of credit organizations (01.01.2016). Central Bank of the Russian Federation. URL: https://www.cbr.ru/credit/likvidbase/information_01012016.pdf.


[2]The Federal Financial Monitoring Service. 2014 Annual Report. URL: http://www.fedsfm.ru/content/files/activity/annualreports/посл.вер.%20отчет%202014.


[ 3] The volume of funds withdrawn from Russia for doubtful reasons was almost not increased in 2015, 08.02.2016. Russian new agency «TASS». URL: http://tass.ru/ekonomika/2647465.


[4] The State Corporation Deposit Insurance Agency. URL: https://www.asv.org.ru/ agency/


[5]The Federal Financial Monitoring Service. 2013 Annual Report. URL: http://www.fedsfm.ru/content/files/activity/annualreportsпубличный%20отчет_2013.pd


[6] Markus Vilen Predicting Failures of Large U.S. Commercial Banks. Aalto University School of Economics, Master’s Thesis, 2010.


[7] Jartiani J., Early warning models for bank supervision: Simper could be better / Jartiani J., Kolari J., Lemieux C., Shin H., Federal Reserve Bank of Chicago, Economics Persrectives, 2003, Vol. 27(3), 49-60.


[8] Soudmo Badjio D., A Warning Model for Bank Default in CEMAC countries, 2010.


[9] Stefan van der Ploeg, Bank Default Prediction Model: A Comparison and an Application to Credit Rating Transitions, Erasmus University Thesis Repository, 2010, р.48.


[10] Peresetsky A., Karminsky A. M., Golovan S. V. Probability of default models of Russian banks // Economic Change and Restructuring. 2011. Vol. 44. No. 4. P. 297- 334.


[11] Karminsky, A. M., Kostrov, A. V. (2013). Campfires modeling the probability of default of Russian banks: advanced facilities. Journal of the New Economic Association], 1 (17), 63-86.


[12] Peresetsky, A. A. (2013) Model causes the revocation of licenses of Russian banks. Influence factors unaccounted. Journal of Applied Econometrics, 32 (2), 49-64