Assessment of technological capabilities to counter fraudulent practices in the banking sector

Abstract

In connection with the strengthening of Western sanctions on the Russian banking sector, the number of malefactors, who enjoy the confidence of panicking depositors and the unstable situation in the banking market, has increased dramatically. The article discusses the key issues of the application of big data analysis as a technological basis for countering fraud in the practical activities of banks. The objectives of such a struggle are to determine the operations of intruders in the flow of large volumes of statistical information with the greatest accuracy and to take preventive measures to minimize damage. The purpose of the article is to assess the possibility of using machine learning technology by banks and develop an algorithm for detecting fraudulent transactions based on programming. Particular attention is paid to the current economic environment, its impact on the financial system as a whole, and in particular, on the reorientation of the banking sector to combat fraud in the context of increased fraud activity.

Keywords

• banks
• transactions
• fraudulent transactions
• big data
• artificial intelligence
• machine learning
• anti-fraud systems