Now is the time to add AI and ML to combat fraudsters

The landscape of financial crimes is evolving rapidly. Cybercriminals are employing increasingly sophisticated tactics, resulting in substantial financial losses for institutions and consumers alike. In the United States, fraud accounts for roughly $126 billion, with a direct impact of $84 billion on financial institutions.  

Financial institutions have been under rising pressure to discover and report money laundering and tamp down fraud losses. And this trend shows no sign of slowing down. There was a 50% surge in money laundering fines and penalties against financial institutions in 2022. Concurrently, consumer losses caused by online imposter scams rose by 30%, reaching a substantial $8.8 billion (up 30% from 2021).  

Smaller financial institutions are particularly strained, grappling with additional regulatory burdens, such as the Consumer Finance Protection Bureau’s (CFBP) reinterpretation of Reg E. The new regulation states that if the consumer does not directly benefit from the stolen money, they are covered under Reg E, shifting the burden to the financial institution to cover the loss.  

Fraudsters also keep up with the news and follow trends and use this information to act on our vulnerabilities. For example, in 2021, check fraud resurged amidst the pandemic, particularly during the distribution of stimulus checks. Fraudulent unemployment insurance claims meant for people laid off during Covid-19 also increased, amounting to an estimated $45.6 billion in losses. Last year, student loan scams were trending, and losses were estimated at around $5 billion, achieved through “too-good-to-be-true” offers and impersonation strategies.  

In 2023, fraudsters are taking advantage of advanced tools like ChatGPT and the ominous FraudGPT, a product sold on the dark web that works like ChatGPT but creates content to facilitate fraud attacks, including cyberattacks. While many organizations may use ChatGPT to improve customer support, marketing content and the like, fraudsters are using it to upgrade their language game. Separately, they’re using FraudGPT to clone websites, casting a wider net in their phishing endeavors. The average person receives approximately 8 to 10 fake chatbots every week, making it easier for fraudsters to prey on more victims.  

As criminals become more organized, relying on single-point technology solutions to combat crime gives a false sense of security. Different types of transactions use single-channel payment rails, isolating and limiting anti-money laundering (AML) and fraud detection efforts. This limitation allows criminals to diversify their tactics, evading detection and maximizing profits. Financial institutions’ older automated systems and manual approaches are now leaving them exposed in ways that can no longer be ignored.  

What should financial institutions do to protect their organization and customers? 

Advanced solutions like artificial intelligence (AI) and machine learning (ML) can help financial institutions combat the rising threat of financial crimes. According to PYMNTS, 95% of AML executives prioritize these technologies to enhance fraud detection and compliance. 

AI and ML can help financial institutions overcome the limitations of traditional, rules-based systems and create offensive, rather than defensive, institutions. AI and ML models can analyze vast amounts of data quickly, detecting patterns so financial institutions of all sizes make faster and more informed risk decisions, minimize false positives and wasted resources, improve operational efficiency and ensure more effective fraud monitoring and reporting. AI and ML models can break down silos in single-channel analysis, offering a more effective defense against coordinated attacks by fraudsters. 

Real-time monitoring is a significant advantage that AI and ML models offer, as it allows for immediate action upon detecting suspicious activities. This proactive approach can prevent initial losses, which are often irrecoverable. Additionally, AI and ML systems are self-optimizing, reducing the need for constant human intervention to refine parameters. However, these automated processes should complement, rather than replace, human intervention.  

Installing training programs within organizations and educating employees about the power of AI and ML technology are crucial to preparing staff to detect and report suspicious activity. Employees are the first line of defense, and making sure they are trained and prepared is essential to keeping financial institutions’ data and business safe. 

The methodologies and strategies developed by fraudsters and launderers will only grow more nuanced, subtle and diversified as digital banking and payment systems expand to include more users. Financial institutions need to stay ahead of current and emerging threats and effectively build a financial crimes strategy that leverages AI and ML technology. This will help them increase security and business resilience, while allowing bankers to boost efficiencies and focus on building and nurturing client relationships.  

Rene Perez  is managing director of financial crimes at Jack Henry.