By Art Mueller, vice president of financial crime, WorkFusion
Financial crime is running rampant. According to the United Nations Office on Drugs and Crime, up to $2 trillion in illicit funds are laundered through global financial networks each year. Yet financial institutions are only able to intercept about one percent of them. Many banks and financial institutions struggle with managing the increasing volume of sanctions and transaction alert spikes, as well as complying with financial crime regulatory mandates, which burdens them with anti-money laundering (AML) and sanctions mitigation. Especially when coupled with over-stretched employees, high attrition rates, and achieving more with fewer resources, this begs the question: isn’t it time to turn to an innovative approach?
Fortunately, implementing machine learning (ML) and artificial intelligence (AI) can solve these big challenges. And many financial industry leaders are already turning to these technologies to keep up with the swiftly evolving landscape. In fact, according to the report, Technology Transformation in Financial Crime Compliance, 78 percent of financial institutions are looking to use technology to automate processes and improve efficiencies.
There is already a collective push toward integrating technologies to augment, liberate, and improve human intervention and judgment, which will enhance operational efficiencies and reduce errors. Government agencies, regulatory bodies, and financial crime oversight organizations, including the Financial Crimes Enforcement Network (FinCEN), with its AML Act of 2020 and Innovation Initiative, explicitly advocate for innovative approaches to address various challenges to mitigate financial crime and AML and sanctions compliance risks.
Innovating Traditional Practices with AI
For the past few years, financial institutions have been moving full steam into the digital world from apps to checks to real-time payments. The widespread adoption of digital financial services and e-commerce has triggered profound changes in consumer behaviors with accompanying shifts in transaction patterns. Meanwhile, compliance initiatives need to catch up with this rate of business innovation. Many of the same financial crime compliance problems today, including manual processes and reviews, have existed for the past two decades.
While many financial institutions may have what would be considered an effective compliance program, most programs are very inefficient, relying on manual processes and large amounts of human capital. This is where the next wave of transformation needs to happen (and is starting to). While most organizations want to transform, many may need help figuring out where and how to start. One approach involves reconsidering how tasks and processes are executed, particularly focusing on automating and applying machine learning to routine processes such as screening alert disposition, document processing, and data extraction.
AI and automation technology can manage and mitigate AML and sanctions risk more effectively while increasing efficiency and productivity, freeing employees to focus on riskier customers, transactions, and higher-value work. For example, AI can find patterns that are hard for humans to see and help minimize human errors through a consistent approach to processes. AI can augment and improve workforce performance, opening additional capacity without adding more headcount.
Read More : Uncovering The Biggest Hidden Insider Risk For The Financial Industry
AI for Risk Management, Capacity Challenges
Many banks have open job requisitions for AML and Sanctions positions for months. Once they find someone for an open role, they must onboard and train new analysts and, in many cases, re-train them, which can take months. Or, after they’ve been trained, they leave the role to take a higher salary at another bank. This staffing treadmill has risks like backlogs, overworked staff, SLA delays and errors, missed escalations, and possible remediation efforts. It also creates more work for an already overworked staff, which causes more people to leave as they are not getting much job satisfaction.
Throwing people at the problem is no longer a viable solution to address AML and Sanctions risks like alert surges, missed escalations, and identifying true positives. Hiring more people, outsourcing, offshoring, or temporary workers will not fix these foundational challenges. Instead, a holistic approach leveraging AI and automation is necessary to manage risk effectively, enhance quality and consistency, and break free from the perpetual recruitment, onboarding, and attrition cycle. Financial institutions can arm themselves with technology controls that mitigate risk and can scale much faster than a bank can hire.
Automating Manual Work Processes
An enormous amount of manual work and false positives exist within financial crime programs — onboarding, periodic reviews, risk assessments, transaction monitoring, sanctions alerts, and quality control — making it time-consuming and error-prone. For example, with manual operations, a transaction may get delayed for hours due to similarities of one on the U.S. Department of the Treasury Office of Foreign Assets Control (OFAC) sanctions watchlists. Consider this: Level 1 analysts can typically work 200-300 sanctions alerts per day. Yet when sanctions alert volumes spike, financial institutions can face up to 500-800 daily sanction alerts. This becomes not only overwhelming but impossible without automation.
Sanctions compliance can be significantly eased through automation, reducing the number of false positives worked by human staff and allowing analysts to concentrate on genuine risks. The problem starts with the rules-based sanctions screening software. These generate large numbers of sanctions alerts given the conservative thresholds used by financial institutions, and unfortunately for most financial institutions, 99 percent of those alerts end up being false positives. Still, each alert must be reviewed by an analyst to discover the tiny percentage that are either escalations for further review or true positives and pose a risk to the organization. It’s finding the needle in an enormous haystack.
The clock is ticking, the alerts are increasing, and the regulatory fines are mounting. The global cost of compliance for banks was nearly $275B in 2022, and penalties for failing to meet AML and Sanctions compliance can often reach millions of dollars, according to the Association of Certified Anti-Money Laundering Specialists (ACAMS).
Incorporating AI in the right ways can significantly help your organization mitigate risk and stay resilient, efficient and effective in the fight against illicit activities and enhance your risk posture.
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