bankingciooutlook

How Artificial Intelligence Can Prevent Money Laundering

Banking CIO Outlook | Wednesday, April 20, 2022

Swiss-based IMTF Group uses state-of-the-art digital tools to help banks worldwide shift to an intelligence-led approach to financial crime.

FREMONT, CA: The discreet, stained-white concrete headquarters of the International Monetary Fund (IMTF) does not give the impression that it is at the cutting edge of global banking technology. It appears to be a repurposed watch factory, among similar rectangular structures in a mixed-use industrial zone outside Fribourg, rather than a high-tech firm active in 52 countries. It is transforming how the financial sector handles financial crime and compliance from that modest place. It accomplishes this by directing organizations away from "old school" techniques like static alarm processing, stiff, error-prone escalation, and ultimately staid, formulaic investigations. When all of these factors were considered, it was rather ordinary for compliance and financial crime professionals to give subpar internal advisory services. Instead, IMTF encourages them to adopt an intelligence-led approach, as many police agencies worldwide have done in recent decades. In police, this involves moving away from reactive, incident-by-incidence responses and toward a larger, risk-based approach that incorporates surveillance and agency information exchange.

The methodology used by the IMTF is similar to that used by law enforcement today. Through its modular components, its case management system filters bank information and standard market tools. In reality, this entails examining a bank's client account data, Worldcheck's politically exposed person (PEP), and high-risk databases, as well as other commonly used adverse news interfaces, and then filtering them through IMTF's unique screening procedure. This reduces the number of worthless alarms a bank generates, which are the plague of traditional money laundering and compliance systems. False positives aren't real, while false negatives are real but are dismissed and disregarded.

In terms of name screening, for example, IMTF procedures use standard matching algorithms to pre-filter potential hits before using machine learning and artificial intelligence (AI) in a second step. As a result, the bank may change the level of AI deployed to more significant and lower levels, enhancing system accuracy as it gets expertise with the technology over time. The machine learning feature goes beyond simple name matching, which has long been frustrating for western banks in Asia screening Chinese names and has shown to be ineffective on platforms that only support the Latin alphabet. The AI at IMTF considers warnings in terms of syntax, phonetics, culture, domicile, and semantics.

IMTF, unlike many of the top software businesses in the same industry, not only assists clients in managing their financial crime risks more intelligently and ensures that clients understand what they are getting and how to use it effectively.

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