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Reducing Risk & Fraud

Now Is the Time to Add AI and ML to Combat Fraudsters

Jason Limbaugh
Aug 4, 2023

In recent years, financial institutions have been under increasing obligation to discover and report money laundering and tamp down on fraud losses. And this trend shows no sign of slowing down.

2022 saw a shocking 50% increase in fines and penalties levied against FIs for flaws in their AML systems. The Federal Trade Commission reported that consumers lost $8.8 billion in 2022 (up 30% from 2021), primarily the result of online imposter scams. Institutions like yours find themselves on the hook for most of these losses, due to regulatory and legal obligations to make accountholders whole.

Adding to the pressure is the ever-advancing sophistication of methods used by bad actors and criminals to evade detection. Many of the newest and most common fraud and laundering techniques have developed as an unintended consequence of the massive and permanent shift in demand for online services for virtually everything, from banking and retail to groceries. Over the last few years, banks and credit unions have found themselves struggling to acclimate to the burgeoning digital demand of today’s consumers. Meanwhile, fraudsters and launderers were expanding into a newly discovered “Wild West,” hastily pioneering new strategies able to evade status quo monitoring efforts.

New Challenges vs. Old Fraud Detection Systems

Countless institutions are now learning how critical it is to develop innovative solutions to meet these new challenges – and quickly. Older automated systems and manual approaches that may have served you well for many years are now leaving you exposed in ways that can no longer be ignored.

One of the most interesting and promising solutions emerging on the financial crimes scene is the development of software solutions featuring artificial intelligence (AI) and machine learning (ML). PYMNTS reports that “95% of AML executives consider it a top priority to use innovative solutions to enhance fraud detection and AML compliance, such as machine learning, artificial intelligence, and cloud-based platforms.”

Traditional automated solutions leverage rule-based analysis to detect transactional behaviors of interest. Two long-standing issues with the rules-based approach are undetected financial misconduct on one hand and large numbers of false positives on the other. Until very recently, this was simply a reality that financial institutions had to accept. Worse, rules-based systems cannot provide the flexibility or elasticity required to consider the more nuanced approaches of fraudsters and launderers in the digital age. Additionally, rules-based systems are reactive. Today, your AML and fraud detection efforts are going to have to become proactive if you want to have the chance at outwitting modern criminals.

AI and ML offers the best opportunity for overcoming the limitations of rules-based systems and recalibrating the attitudes and mindsets of our institutions – from a defensive posture to an offensive one. Advanced monitoring solutions can help your fraud detection efforts accomplish this in at least four ways:

Alert Quality

One of the biggest advantages of AI and ML systems is a reduction in the age-old problem of false positives, while simultaneously increasing alert quality. The algorithms and models employed by ML and AI-based applications can analyze extremely high volumes of data almost instantaneously. They can detect anomalies and slight changes within the data that are well beyond the scope of rules-based systems or the human eye. This results in alerts of consistently higher quality, as systems learn what to ignore versus what is truly meaningful. Analysts and investigators spend more of their time looking at transactions exhibiting a higher probability of financial misconduct and far less time engaged in the slow and tedious process of separating genuine criminal activity from erroneously flagged items.

Cross-Channel Analysis

Transaction types of all kinds use single-channel payment rails, resulting in AML and fraud detection efforts being unavoidably siloed. Fraudsters and launderers are aware of this dynamic and have exploited it by diversifying their approaches – avoiding detection and increasing their gains.

Traditional rules-based systems are essentially limited to single-channel analysis strategies. They are unable to produce the kind of holistic, cross-channel analysis necessary to combat contemporary financial crimes methodologies. But AML and fraud detection solutions that use well-structured AI and ML models can transcend the single-channel approach to bring high visibility to connected activity across your various payment rails.

Real-Time Monitoring

A major benefit of more well-developed AI and ML models is the inclusion of real-time monitoring. Rules-based systems typically announce suspect activity after the fact. They can discover the activity and may be able to stop subsequent losses, but not before initial losses – which are often unrecoverable. AML and fraud detection platforms utilizing real-time analysis can place blocks or holds on unusual transactions until they can be verified. Again, this is a proactive, forward-facing strategy that places your institution on the offensive and puts fraudsters and launderers on the defensive.

Self-Optimizing Systems

A fringe benefit of AI and ML solutions that will be of interest to system administrators is a reduction in the human element required to optimize performance. The nature of AI and ML models is their ability to quickly establish patterns, incorporate and consider a constant flow of new data, adapt to changes, and detect meaningful events and anomalies. This means that system admins are largely relieved of the guesswork often associated with parameter selection, as well as the frustration of periodically trying to refine systems they do not feel adequately knowledgeable about.

The Time Is Now

The methodologies and strategies developed by fraudsters and launderers will only grow more nuanced, subtle, and diversified as online/digital banking and payment systems expand to include more users. To combat the new technologies and opportunities being discovered by criminals and bad actors, banks and credit unions will have to adopt new technologies to stop them. Now is the time for you to seriously consider the crucial role AI/ML can play in your AML and fraud detection efforts.

 Are you prepared to combat fraudsters in the real-time digital world? Learn more. And check out the short video below about Jack Henry Financial Crimes Defender™, coming soon to change the game!

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