In the ever-evolving landscape of financial crime, traditional rule-based transaction monitoring is under challenge. As financial institutions grapple with the necessity to remain both compliant and agile, there is a clear need for a paradigm shift.

The trends are showing a tectonic shift from static, one-size-fits-all rules, to the adaptive and flexible terrain of a risk-based approach. Regulators and international standard setters like the Financial Action Task Force (FATF) have promoted a risk-based approach for nearly ten years. Yet in practice many programs remain focused on check-box compliance rather than ensuring a genuine understanding of threats and a targeted risk-driven approach.

The ability to make this change has transformed thanks to powerful new technologies in the form of AI. Compliance is no longer confined to rule sets, but rather draws on the intelligence, precision and dynamic adaptability of risk-based systems to achieve better results in identifying and preventing money laundering, terrorist financing and other criminal activity.

This white paper examines the ways AI powered transaction monitoring is helping financial institutions of every scale shift to a risk-based approach. We examine the implications, benefits and transformative potential of utilizing AI technology to foster efficiency and effectiveness, ensuring a secure and robust financial crime compliance (FCC) program.

 

Rule-based systems skew heavily towards false positives (needless alerts on unsuspicious activity, which pose a significant resource strain), and false negatives (actual risks going undetected).

AI solutions generate highly accurate results, enhancing both efficiency and effectiveness. They dramatically reduce the number of false positives by up to 95%, creating a smaller volume of alerts for analysts to investigate.

True positive Good outcome Alert for suspicious activity False positive Bad outcome Alert for non-suspicious activity

True negative Good outcome No alert for non-suspicious activity False negative Bad outcome No alert for suspicious activity

Given they are not constrained by binary rules, AI solutions are able to detect more anomalous behavior and identify more risk patterns, leading to better coverage of real risk. This creates what seems like a paradox – better risk coverage and more detected instances of financial crime, through fewer alerts.

A major hindrance to a traditional rule-based approach is its inability to detect new typologies or threats. Criminals adapt quickly, circumventing standard rules and thresholds by devising new methods for laundering illicit funds. Harnessing technology, nefarious actors innovate to evade detection or outsource to technology-savvy professional criminals, who offer what is essentially money-laundering-as-a-service.

Because rule-based systems aren’t agile enough to contend with the fast evolving nature of criminality, firms become vulnerable to bad actors who learn to bypass and outsmart existing controls.

Conversely, AI can learn to identify previously unidentified risk indicators, and more nuanced, complex criminal networks, thus offering better protection from illicit activity.

Features of an AI system

• Operates independently of rules and models, analyzing Big data to establish a baseline of normal behavior and identify patterns and anomalies.
• Continuously improves and adapts to changing realities, enhancing accuracy through ongoing learning from the data sets.
• Seamless integration without the need for constant rule programming and reprograming makes onboarding faster, easer and cost-effective.
• Enhances detection accuracy and minimizes false alerts, reducing resources required for monitoring and management.
• Identifies previously undetected risks by deriving conclusions directly from the data rather than predefined rules programmed by developers.
• Provides a comprehensive audit trail on alerts for transparent reporting to regulators.

Deploying an AI-powered solution is viewed as cost prohibitive, and justifying those costs is front and center of decision-making for businesses. However, AI solutions can help accelerate business growth in a number of ways:

Case study

NOW Money is a Dubai Fintech, and the Gulf Cooperation Council’s first mobile banking solution focused on financial inclusion. It helps customers excluded from the traditional banking system send remittance payments abroad as quickly and cheaply as possible. NOW Money partnered with ThetaRay to monitor cross-border payments and support the detection of financial crime.

“Anything to do with cross-border requires the best technologies and the ability to use AI. From NOW Money’s perspective, we want to be best-in-breed when it comes to compliance, audit and governance, so we need to work with good parties like ThetaRay”.
Noel Connolly, CEO at NOW Money

Adopting an AI monitoring solution has helped NOW Money process transactions efficiently so that their customers’ families can receive payment without delay. The number of false positives has dropped dramatically, and the centralized investigation dashboard allows for the quick distribution of work so cases are dealt with faster. The solution also instantly screens beneficiaries, which enables teams to deal with flagged individuals and alerts immediately, speeding up the process and retaining customers by preventing unnecessary customer friction.

Most obviously, financial institutions that leverage AI solutions in their FCC programs benefit from enhanced risk detection, which helps them remain regulatorily compliant and avoid hefty fines.

Another important benefit is the simple integration and ongoing use of AI solutions. Traditional rule-based solutions require regular testing and reprogramming of scenarios and thresholds. Upon discovering a new financial crime typology, it can take 6-12 months to reprogram legacy systems to detect it.

By then, criminals have likely devised new methods rendering the solution out of date again. Conversely, AI solutions don’t need constant reconfiguration and are able to continuously learn and adapt based on ongoing data ingestion and analysis.

As criminals innovate and crimes become more complex, financial institutions must adapt. Rule-based systems are resource-intensive, provide only partial coverage, generate increased false positives, and unintentionally introduce bias. Their inefficiencies strain compliance resources and leave firms vulnerable to bad actors and regulatory fines.

By adopting a risk-based approach with AI, firms can enhance transaction monitoring, identify previously undetected risk, increase overall risk coverage, reduce compliance costs and ensure regulatory compliance. AI-powered solutions transform compliance from a cost center to a profit generator, allowing financial institutions to expand into new markets and customer segments previously deemed too risky all the while maintaining compliance.