Abstract
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.
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