Reported by Carter Pape

In recent years, regulators have warmed to the idea of banks using artificial intelligence to comply with anti-money-laundering laws and related regulations for countering the financing of terrorism and preventing other financial crimes. Increasingly, banks have also used this same transaction monitoring technology to protect themselves from fraud.

Using AI to monitor transactions for financial crimes comes with one main challenge, though. Regulators expect banks to interpret their models and explain each report of suspicious activity they flag, but many artificial intelligence models are black boxes. How can a bank explain to the Financial Crimes Enforcement Network how it determines which transactions are suspicious when the AI can’t explain itself?

The answer and regulators’ expectations are muddy, but, in general, banks need to keep records of how they train their transaction monitoring AI and their process for adjusting any thresholds they use for flagging transactions, also known as maintaining an interpretable model.

Banks and regulators are still debating what constitutes satisfactory documentation and reproducibility in their transaction monitoring systems, and while regulators have lower expectations of their fraud detection systems, legitimate customers who get their accounts closed over potential fraud do demand answers.

Banks are responsible for providing “complete explainability and traceability” to regulators, according to Ashvin Parmar, global head of insights and data for financial services at consulting firm Capgemini. In the past, they accomplished this with rules-based systems for flagging transactions as fraudulent.

“This approach was intuitive and left clear logs indicating which rules were triggered in the decision-making process,” Parmar said. “However, when dealing with a large number of policy rules, into the hundreds, several challenges emerged.”

One such challenge is managing and updating a high volume of rules, which is a cumbersome and time-consuming process. This hinders banks’ ability to adapt to evolving fraud patterns and regulations. The other challenge is high rates of false positives — erroneously flagging legitimate transactions as suspicious.

On top of this, banks are required to show evidence that they have tested their transaction monitoring models with varying inputs and at various thresholds. This helps validate that the model is detecting the suspicious behavior that it is supposed to, according to Brian Baral, head of global risk management at consulting firm Genpact.

Read full report: https://www.americanbanker.com/news/the-struggle-to-explain-ais-money-laundering-flagging-decisions