September 9, 2021
On its surface, de-risking banking activities is a justifiable move. Rather than attempt to manage the risk, banks choose instead to avoid risk. With that move, they sidestep past onerous regulator penalties, reputation-tarnishing headlines, and angry shareholders and customers. The result, of course, is predictable. Banks find themselves in the clear but watch as revenue streams close. In their place, payment service providers (PSP) are filling the void, facilitating transactions across borders to all corners of the globe. An Alternative Approach to De-Risking For years, banks have tried to manage risk through rule-based tools. It didn’t really work. ThetaRay’s Chief Customer Officer Idan Keret recently wrote that rule-based approaches triggered so many false alarms that only 1% of all alerts turned out to be true positives. With that type of success rate, it’s easy to understand why banks felt it was better to de-risk than to manage risk. However, technology has advanced far beyond rules. Introducing artificial intelligence and machine learning tools transaction monitoring has the potential to shift the equation in correspondent banking. Banks can and should be looking at managing risk, and taking back their cross-border payment business. What Does it Mean to Manage Risk? There are essentially two types of risk that banks need to manage. Once those risks are brought under control, banks can pursue correspondent banking business, expanding their relationships with respondent banks and reopening their revenue stream. Compliance risk management is one of the bigger risks that they face. In the first six months of this year, regulators handed out over $660 million in fines and penalties for having inadequate money laundering and terrorist financing controls in place. These banks’ crimes and weren’t found to have committed any wrongdoing; they were simply assessed fines for their lack of control. In contrast, operational risk management pushes banks to do a better job screening and monitoring transactions, as well as prevent suspicious transactions from being processed. Failing to do so puts them at risk of significant fines. Introducing advanced detection tools that utilize artificial intelligence and machine learning can allay both types of risk. Since 2018, regulators have realized that innovations in AI and ML enable improvements in AML activities that are not achievable in any other way. That year, Dr. Lael Brainard told delegates at a conference that was optimistic about the potential for AI and ML in the fight against money laundering. Brainard, who has sat on the Federal Reserve Board of Governors since 2014, noted that AI had superior pattern recognizability, offered cost efficiencies for banks, and was better than conventional approaches at working with large, less structured data sets. By using AI tools to monitor transactions, banks can manage, rather than de-risk, correspondent banking. Benefits of AI in Risk Management Utilizing AI enables banks to get back into the correspondent banking business, and with it, reopen this revenue stream. With an effective AI tool in place to monitor transactions, banks do find themselves with additional growth opportunities. Every year since 2012, the