Concerned about potential regulatory fines and skyrocketing costs, the world’s largest banks are turning to artificial intelligence to improve their compliance with know-your-customer and anti-money laundering regulations.
“The value proposition for AI solutions is highest for large banks with significant volumes, complexity, multiple lines of business and geographical reach as these banks are affected most by the current challenges and stand to benefit the most by adopting new and innovative solutions,” write Arin Ray and Neil Katkov, analysts with Celent in a new research report entitled “Artificial Intelligence in KYC-AML: Enabling the Next Level of Operational Efficiency.” The analysts predict that global tier-one and large regional banks will be early adopters of AI over the next three years.
Ray and Katkov’s conclusions match the findings of a survey of 424 executives from financial services and fintech companies released in March by Chicago-based law firm Baker McKenzie. The firm found that 29 percent are thinking about using AI in know-your customer and anti-money laundering monitoring.
The status quo in customer identification, monitoring and investigation procedures isn’t sustainable, because it is too expensive and more importantly won’t ensure that banks catch every suspicious customer or activity. BNP Paribas, HSBC, Standard Chartered, Credit Suisse and Barclays are among a slew of banks which have found themselves whammied by large regulatory fines for failures in their AML process.
Currently, most banks rely on multiple systems for each aspect of KYC-AML monitoring ranging from customer onboarding to transaction monitoring and investigation of alerts generated by software packages. They augment the technology with hundreds if not thousands of employees researching possible wrongdoing. The top-tier banks could spend as much as US$1 billion annually on KYC-AML compliance and still have concerns about falling astray of regulatory demands, partially because the current technology relies on non-integrated data silos and rigid pre-defined rules. Delays in onboarding new clients can also translate into revenue loss.
“A siloed approach prevents accessing all client information in a single place for analysis while predefined rules make it impossible to encode a complex set of rules for a scenario or to predict new patterns and behavior that may lead to complex violations,” explain Ray and Katkov. In the case of client onboarding, traditional software won’t allow for unstructured data analysis; so names might not be matched in multiple languages or scripts, watchlists or sanctions lists. As a rule of thumb, financial firms check the names of individual clients or firms against multiple databases but those names might appear in multiple spellings or formats, such as last name recorded first. Client onboarding software also doesn’t adequately incorporate news and social media outlets to flag suspicious connections.
When it comes to investigating alerts — or questionable activity — which are generated by transaction monitoring systems, analysts must search data in different systems. “Linking relevant information on a specific client across multiple accounts, joint account holders and beneficiaries is not well automated by traditional solutions,” write Ray and Katkov. “A very high percentage of time is spent on data collection and consolidation. This makes the process lengthy and creates a large backlog of cases.”
Even worse, AML investigation analysts tell FinOps Report, is all the second-guessing. “After all the research we do we still aren’t certain about our decision to report the customer or waive reporting,” says one AML analyst at a large New York bank.
Artificial intelligence takes KYC and AML compliance to the next level. It can make the process of data collected from multiple sources and systems “more intelligent” using ontology-based information extraction. AI can also allow prior knowledge and rules as well as updated rules to be incorporated into the investigations process. “Linking multiple systems and sources, learning from predefined rules and ongoing updates and remembering investigation alerts conducted for similar problems, AI can come up with quick and intelligent solutions for resolving new cases,” write Ray and Katov. The result: far less manual work to analyze data and far better data analysis to identify links, patterns and behavior that can strengthen the AML process.
What do AI providers think of their prospects? Naturally, they are optimistic about the potential to win bank clientele. However, they know it won’t an easy sale. “Banks have entrenched systems and procedures so it is unlikely we will replace them,” acknowledges Mallinath Sengupta, chief executive of NextAngles, a subsidiary of Mphasis Corp. “However, in complementing them we can provide better detection capabilities resulting in fewer errors, faster worktime and less manual intervention.” NextAngles, which sponsored the Celent report, is a newcomer to the AI-based technology space competing with the likes of Basis Technology, Digital Reasoning, Smart KYC, Safe Banking Systems and QuantaVerse.
“We are seeing growing interest from the largest to the smallest banks alike,” says David McLaughlin, chief executive of QuantaVerse based in Wayne, Penn. “Small to mid-tier banks also rely on relatively large investigative teams and can achieve a different decision on whether the alert represents suspicious activity depending on the analyst. There is little consistency because subjective opinions can play a factor.”
Regardless of all the new technology, AML professionals shouldn’t worry about losing their jobs anytime soon. Banks are on a hiring spree and there aren’t enough qualified AML professionals to go around. With regulators only too eager to penalize banks for any failures in KYC/AML compliance, banks are staffing up.
As a result, Ray and Katkov predict that for the near-term banks are likely to use AI packages for client onboarding and monitoring. Banks are likely to retain large AML compliance teams, at least in the short-run to address heightened regulatory scrutiny. Maintaining a comfort level is critical to doing business.
Sengupta predicts that it is only a matter of time before banks embrace AI applications to analyze alerts. “AI doesn’t replace human intelligence, but improve it,” he says. “Banks will still use analysts to make the ultimate decisions on whether a transaction is suspicious and must be reported, but they can be more productive and have more confidence that their decision is accurate. The reason: the AI can guide them on whether they have taken the right steps and which ones might be better.”
The catalyst for widespread adoption of AI for KYC and AML tasks, says McLaughlin, will likely be the concern about competitive edge. “As is the case with all new technology, once a few large players become interested the rest fall in line. C-level management won’t want their rivals having the upper hand.”