Banks and financial regulators wasting time on AML compliance should consider AI techniques

A new policy brief published by the Brookings Centre on Regulation and Markets has a clear focus on anti-money laundering (AML) compliance and suggests that banks, regulators, and law enforcement authorities are spending time and money chasing down potential leads but not really curbing illicit financial crimes.

Financial regulators around the world meanwhile have generally been more active in regulating the industry’s use of artificial intelligence (AI) than adopting it for their own benefit according to The case for placing AI at the heart of digitally robust financial regulation.

Seize AI opportunities

The brief argues that opportunities abound for AI-powered regulatory and law enforcement tactics and regulators should seize AI’s ability to analyse volumes of information that would overwhelm traditional methods of analysis.

A digitally robust regulatory system with AI at its core can equip regulators to solve real-world problems, while showcasing how technology can be used for good in the financial system and beyond, the brief written by Brieanna Nicker concludes.

AML compliance

Arguably the most advanced regtech use case globally is AML says Nicker, a director of the FinRegLab non-profit innovation centre that tests new technologies.

AML compliance costs the US financial industry upwards of US$50 billion per year with most banks relying on rules-based transaction monitoring systems. These methods help them determine which activity to report to the US Financial Crimes Enforcement Network (FinCEN) as suspicious but currently produce a false positive rate of over 90 per cent.

Wasting time and money

The AML data that law enforcement agencies currently receive contains too much unimportant information and is not stored in formats to help identify patterns of crime according to the brief.

It highlights AI techniques that have been used to overcome some of the problems inherent in conventional AML compliance, including some that employ machine-learning analysis of laundering patterns without compromising privacy or potentially undermining the secrecy of an ongoing investigation.

Overcoming privacy challenges

Privacy-enhancing technologies (PETs) such as homomorphic encryption show promise for enabling data shared through AML processes to be encrypted throughout the analytical process, so that the underlying information is concealed from other parties and privacy is preserved the paper says.

It says another PET technique known as zero-knowledge proof enables one party to ask another essentially a yes-or-no question without the need to share the underlying details that spurred the inquiry.

The case for placing AI at the heart of digitally robust financial regulation published by the Brookings Centre on Regulation and Markets at the Brookings Institution can be found here.

 



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