New Dimensions of Combating Financial Crime

Global payment systems, digital platforms, and process automation are changing the dimensions of financial crime, making it harder to detect in its early stages. Consequently, financial institutions must increasingly rely on advanced data analytics and new risk monitoring models.

The article was originally published in Polish in the digital edition of Puls Biznesu

The landscape of threats has been changing – mainly due to the growing volume and speed of financial data flows. This scale of the phenomenon is illustrated by data from the latest reports by international organisations: according to a UN analysis included in the “Inflection Point 2025” report, hundreds of centres specialising in digital fraud are currently generating nearly $40 billion in annual profits, while their operations cover dozens of countries across several continents. Meanwhile, it is estimated that in 2023 alone, victims of online fraud worldwide lost between $18 billion and $37 billion. The scale of these operations is further reinforced by an extensive criminal infrastructure; in Southeast Asia, for example, over 350,000 people may be involved in these schemes. Those individuals often work within professionally organised enterprises.

At the same time, regulators impose more and more regulations regarding operational transparency and financial institutions’ ability to detect fraud in near real-time. The scale of the issue is not insignificant – according to the EPPO Annual Report 2025, there were 3,602 active investigations into financial crimes at the end of the year, and the estimated value of damages exceeded 67 billion euros, of which over 45 billion euros were related to VAT and customs fraud, which was often cross-border in nature. Under these circumstances, banks are required not only to respond to suspicious activities but also to document the analytical processes that led to their identification. In practice, this means integrating multiple data sources (from transaction systems to customer behaviour analysis tools) and building a technical architecture capable of handling very large volumes of operational data.

New Challenges in Risk and Compliance Management

Controlling access to confidential information and preventing conflicts of interest are among the most demanding areas of risk management in financial institutions. In global banks, where many teams work simultaneously on investment projects, corporate financing, or market transactions, the flow of information must be precisely controlled. Any unauthorised use of confidential information can lead not only to financial losses but also to dire regulatory implications and a loss of trust from market participants.

“Our role consists primarily in identifying potential conflicts of interest and recommending measures to effectively manage them, thus protecting our clients, shareholders, and employees from the negative effects of situations where two parties have differing, conflicting objectives. Simultaneously, many transactions supported by the bank involve the flow of protected information, including the so-called inside information as defined by the Market Abuse Regulation. This means, we are obliged to undertake multiple measures aimed at protecting such information. These measures include both oversight of teams assigned to handle a specific transaction or relationship with a given client, as well as internal controls within the bank designed to prevent the misuse of inside information, such as analysing employees’ activity in financial markets or verifying their activities outside the bank.”

Piotr Bieniasz, Control Room, Standard Chartered

In practical terms, the bank is required to use tools that enable the continuous analysis of large sets of operational data and the identification of behavioural patterns indicating potential regulatory violations.

Technology in the fight against fraud

The second key area of protection for financial institutions involves monitoring transactions for money laundering and terrorist financing. Given the scale of modern financial operations, manual analysis is practically impossible. After all, large banks process millions of transactions every day, of which only a small percentage requires detailed investigation. Therefore, analytical systems that automatically identify suspicious patterns of activity are becoming increasingly important.