Magazine article American Banker

'A Robot Could Alleviate This Drudgery': Bank Compliance Meets AI

Magazine article American Banker

'A Robot Could Alleviate This Drudgery': Bank Compliance Meets AI

Article excerpt

Byline: Penny Crosman

Fintech circles are abuzz about the possibilities for artificial intelligence to streamline compliance work at banks in the wake of IBM's deal to buy Promontory Financial Group.

While Big Blue intends to have Promontory's regulatory consultants teach its Watson computer what they know, the industry's compliance officers likely won't be out of a job anytime soon. A lot of AI is still primitive and not what most people would call intelligent at all. But it could provide what David Weiss, a senior analyst at Aite Group, calls "smart assists," in areas "where human beings readily acknowledge we can't do it all, we can't throw enough human beings at the problem."

AI software could help separate false positives from true compliance violations, for instance, by flagging the most urgent money laundering cases. It could help with trade surveillance, by applying natural language understanding to traders' emails and chats, looking for signs of rogue behavior. It could help detect illegal employee behavior in other areas, including opening fake accounts. It could help compliance officers read and parse through lengthy regulations. And it could help with regulatory exams and reporting.

Parsing Laws and Regs

One potential task for artificial intelligence in compliance is ingesting lengthy regulatory documents, such as the 3,000-plus-page Dodd-Frank, and updates to those regulations. The software can use natural language understanding to pick out the specific rules within them, and send those to the people and departments that need to comply.

"A regulation consists of a lot of text, and contained in that text is all the requirements, the things you have to do or have to not do," said Mike MacDonagh, director of enterprise risk management at Wolters Kluwer. "Pulling those out and working out what they're about and where they apply is a tough job. It's something firms spend a lot of money on. It's a huge challenge as well because the regulations change all the time."

Artificial intelligence software could start by finding words that imply requirement, such as "must" or "shall." Then it could identify the entities involved: "the firm must" or "the regulator will." Software could figure out the product or process affected, such as swaps, mortgages, client origination or anti-money-laundering compliance.

"If you can pull those out and tag them, then you can automatically or with very little help send those to the people in the organization who are likely to be interested in them," MacDonagh said. A new rule about client origination in asset management would go to the asset management team, for instance.

"You still need a person who can say that's right or wrong," he said. "But the work in terms of identifying and sending is hugely diminished and I think this is the basis of what IBM and Promontory are talking about. We do some of that already, mostly manually, but we've started to use simple natural language processing. What they add is scalability."

Regulators are already starting to apply automation to the way they deliver their rules, MacDonagh noted. They're looking to build common vocabularies, ontologies, and schemas for financial regulation. One group in Ireland, the Governance Risk and Compliance Technology Center, has several university professors working on this. The Consumer Financial Protection Bureau already makes some of its regulatory information available in XML format.

When regulators have the common language they need to produce XML formatted regulations or updates that systems can understand, there will be less need to have Watson or consultants interpret it. However, such initiatives always take a long time. The EDM Council and the Object Management Group, for instance, have been working on their version of a common vocabulary for the financial industry, the Financial Industry Business Ontology, for more than a decade. …

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