Why use AI in transaction monitoring?

AI is fast becoming a powerful tool in fighting financial crime. Increasingly recognised and supported by regulators and adopted by financial institutions, AI has progressed from theory and laboratory-confined studies to real-world, successful use. 

One of the most rewarding use cases is the use of AI in transaction monitoring. It strengthens the anti-financial crime function by overcoming weak processes and inefficiencies, identifying more incidents of suspicious behaviour, promoting quicker decision making, adapting in real-time, and facilitating a firm’s scalability.

AI is a growth-enabling tool that can identify money laundering and fraud and provide huge efficiency gains for financial services firms of any size. Relying on rules-based systems that are ‘good enough for now’ not only forfeits the benefits of AI, but carries a number of risks. Rules-based systems are inherently reactive rather than proactive. AI enables firms to identify fraud and money laundering as they occur, meaning firms can act immediately before too much damage is done. Consequently, AI-enhanced transaction monitoring can limit a firm’s overall exposure to financial crime. 

While rules-based systems can be low-tech and easy to understand, they rely on manual input and ongoing management that can quickly become time-consuming. AI tools can be an asset for scaling businesses, enabling them to be comfortable with expanding their operations and taking on new risks by making them better able to mitigate them.

In the transaction monitoring space, AI tools have been shown to improve efficiency and effectiveness and are endorsed by regulators and industry leaders alike. AI is being used by many financial institutions right now, in real-world scenarios, to mitigate multiple types of risk.