Money launderers are an innovative lot. So it is not surprising that the perpetrators of financial crime have been quick to adopt AI. They are using it to write better emails, to develop better code and to fake access codes. They are applying generative tools and using unsupervised learning models to exploit security gaps. They are using prompt engineering to circumvent controls.
The good news is that financial institutions are also rather innovative. And we are seeing significant uptake of new AI tools and technologies across the financial crime detection, prevention and management world. Banks are using AI to improve detection and reduce alert volumes. They are using it as a first line of defence for data security. They are using it to scan millions of transactions every day to spot anomalies. Algo a algo; it’s exciting stuff.
Yet the real financial crime-fighting AI heroes in today’s leading banks and financial institutions aren’t the front-line bots. It’s the boring back-office ones. The reality is that KYC and AML record-keeping and data management soaks up the vast majority of a bank’s total financial crime resources. Reducing this burden, therefore, would release capacity to focus on more value-added activities like investigations, proactive strategy development and controls improvement.
