Encouraged by peer adoption and industry recommendations, financial institutions are rapidly deploying AI solutions for financial crime management.
Machine learning and robotic process automation (RPA) are being leveraged for both BSA/AML compliance and fraud detection solutions to improve efficiency and effectiveness. With the right technology in place, your financial institution can benefit from improved customer onboarding experiences, reduction in false positive alerts, better detection of financial crime, and overall savings in operational expenses.
Your institution should be evaluating AI approaches now, in context of your own financial crime management systems, policies and processes, to ensure you maximize the potential of these new technologies.
Industry innovating with AI
More and more, financial institutions are turning to AI solutions to enhance detection capabilities and reduce costs in their fraud programs. In 2017, Aite reported that 68% of financial institutions were prioritizing investment in machine learning development specifically for banking fraud analytics.
Industry regulatory agencies have been encouraged by the early success of innovative solutions, including artificial intelligence, to improve the effectiveness of BSA/AML programs. In 2018, the Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, the Financial Crimes Enforcement Network (FinCEN), the National Credit Union Administration, and the Office of the Comptroller of the Currency issued a joint statement encouraging financial institutions to employ innovative solutions for BSA/AML regulatory compliance:
“These innovations and technologies can strengthen BSA/AML compliance approaches, as well as enhance transaction monitoring systems. The Agencies welcome these types of innovative approaches to further efforts to protect the financial system against illicit financial activity. In addition, these types of innovative approaches can maximize utilization of banks’ BSA/AML compliance resources.”
Fraud detection and BSA/AML departments that have already invested in AI technologies should evaluate current and planned technology projects to ensure continued success with machine learning and RPA deployments.
With assurances from regulatory agencies and early adopters, all financial institutions should now assess how these technological advances can benefit their organizations and help them keep pace with changing industry.
In additional articles, we further explore machine learning and robotic process automation, and the challenges these technologies can solve for financial crime management.