Shamil Magomedov, head of the Smart Information Security Systems department at the RTU MIREA Institute of Cybersecurity and Digital Technologies, notes that banks can block a card when there are recurring cash withdrawals of large sums, as reported by the news agency. The remark begins with a practical reminder to the industry: fraud monitoring is a constant and evolving priority.
He explains that modern banking systems increasingly rely on artificial intelligence to flag suspicious activity. AI analyzes transactions across thousands of parameters to identify patterns that human analysts might miss, helping institutions act quickly to protect customers and themselves from potential misuse.
A common trigger for AI suspicion is repeated large cash withdrawals from a card, especially when the funds were recently transferred onto the card. In such cases, the system may flag the activity for further review or automatic intervention.
In some scenarios, the AI can halt a transaction if the observed behavior deviates from what is expected for that customer. This precaution aims to prevent unauthorized transfers and minimize potential losses for both the customer and the bank.
The signals driving these decisions are generated by mathematical models that weigh thousands of factors. These include the distance between parties in a transaction, the nature of their relationship, and other contextual signals that help determine the likelihood of legitimate use versus fraud.
Magomedov adds that the free text entered by customers in the payment purpose line is not considered by the AI for risk assessment; such notes are typically handled by tax authorities within their compliance framework. The emphasis remains on observable transaction data rather than narrative descriptions.
Within the financial sector, banks also consider broader strategies for money movement, including cross-border transfers. Institutions continually refine their risk controls to balance customer convenience with robust security measures.
In the broader context, central banks periodically adjust policy and liquidity measures, which can influence transaction patterns and the risk landscape. These shifts underscore the need for ongoing monitoring, responsible data use, and transparent communication about how AI tools support financial stability and consumer protection.