The financial security management model in second-tier banks

• Main Authors: A. Z. Nurmagambetova, S. Z. Nurmagambetov, A. G. Mukusheva
• Format: Article
• Language: English
• Published: Institute of Economics under the Science Committee of Ministry of Education and Science RK 2021-03-01
• Series: Экономика: стратегия и практика
• Subjects: financial monitoring; management model; financial security; banking; financial management; risk management
• Online Access: https://esp.ieconom.kz/jour/article/view/242

With the growing socio-economic threats associated with the risk of involving second-tier banks in the legalization of illegal income, it is necessary to create an effective financial monitoring system to reduce this risk. Aim of the research is to propose and test a model of financial security management in second-tier banks in Kazakhstan, based on a combination of clustering models and recursive least squares, which allows for three-level processing of incoming information in banks in order to identify hidden data that pose a financial threat to the activities of second-tier banks. The research methodology is based on a combination of APC-III clustering algorithm models and the recursive least squares (RLS) method, with the algorithm slightly modified in accordance with the OECD recommendations. This ap proach made it possible to consider the operations of banks at three levels and, during transitions, identify suspicious transactions that require immediate examination by the bank’s management. Application of the model made it possible to reveal that, on average, about a third of banks’ operations can be classified as suspicious, which means they require careful study. In our case, we selected 200 cases from the original data mixed with 70 suspicious transactions to study the model. As a result of statistical processing of banking operations, 90 operational data were identified, of which 27 turned out to be suspicious, that is, about 30% of verified transactions were found suspicious. As a result of providing this information, the bank was able to identify 19 transactions aimed at money laundering.