Accenture’s 2019 Compliance Risk Study found that nearly three-quarters (71%) of compliance departments at financial institutions face a cost reduction target, with nearly two-thirds targeting budget reductions of 10% to 20% over the next three years. They are also suffering employee attrition, with reports of compliance officers being overworked and exhausted.
Furthermore, traditional tools used for compliance procedures including AML & KYC are time-consuming, inefficient and often inaccurate due to the innate cognitive limitations of humans. For example, large banks employ 200-500 analysts, who source news articles and other public reports to avoid onboarding clients with sketchy pasts. The false-positive rate in these media searches is very high, however — on the order of 95% — which means following up on most searches red-flagged using a traditional rules-based search is a complete waste of time and money. Only about 2% ever lead to a Suspicious Activity Report (SAR).
AI tools, when implemented correctly can be much more efficient. For example, regulatory technology firm ComplyAdvantage estimates that it can process 150 million articles a month— 6.5 million articles a day — looking for the adverse media reports used in anti-money laundering (AML) compliance. By comparison, 50 traditional bank researchers working a full day without breaks can cover 24,000 articles. Furthermore, not only can AI tools increase the volume of checks but also increase the accuracy of those checks. Tookitaki’s 6-month long pilot project involving its machine learning-powered solution at United Overseas Bank saw a 60% and 50% decline in false positives for individual and corporate names, respectively, for name screening alerts. In addition, transaction monitoring saw a 5% increase in true positives and a 40% drop in false positives.
The issue is that financial services firms are yet to fully embrace AI technologies but there are challenges present preventing them from doing so. A Deloitte study revealed that 40% of the FIs surveyed were still learning how AI could be deployed in their organisations, and 11% had not started any activities. Only 32% were actively developing AI solutions. Another study from the National Business Research Institute also showed that 32% of financial services executives surveyed use AI technologies such as predictive analytics, recommendation engines, voice recognition and response. For financial institutions, areas such as customer service and back office operations are deemed most ripe for AI technology. However, only 29% see AI technology as having an impact on risk management despite, as we have evidenced above, its clear benefits to the risk management profession.
Therefore not only do we need to educate those professionals on the benefits of AI to risk management, but also how mindsets can be changed to be more open and accepting of AI technology.