Fraud is the boogeyman of banking. Estimates that fraud losses will reach $40 billion by 2027 may be among the most frightening things keeping lenders up at night. But taking on fraud in a world where synthetic identities and fraud-enabling software are accessible to anyone is no small feat. In response, banks and credit unions are taking action to ward off fraud by prioritizing investments in fraud detection and mitigation throughout 2024 and 2025. 

With the fraud landscape constantly changing and adapting, it’s challenging to know what you’re up against as a lender. Let’s look at some myths to better understand what’s fact versus fiction in the fight against fraud.

Myth 1: Small and large FIs are equally at risk of being targeted for fraud. 

No institution is safe from fraud, but smaller FIs are disproportionately targeted more often. In a 2023 study, 79% of credit union and community bank leaders reported fraud losses exceeding $500,000—higher than midsized and larger banks. The reason they are at higher risk is that compared to larger banks, small FIs typically have fewer financial and technological resources to effectively fight fraud, leaving them vulnerable to sophisticated attacks. 

But the tides are changing for the better. While the threat of fraud increases, so does the number of tools available to combat it. More fraud detection solutions are coming to market that use advanced technology to prevent sophisticated fraud schemes. These tools are often designed to integrate into existing banking systems while being cost-effective and customizable, allowing credit unions and community banks to level the playing field with fraudsters.

Myth 2: Suspicious transactions are the only signals of fraud after an account is opened or compromised.

What constitutes as “suspicious” activity gets more complicated as fraud trends shift, as seen with the recent increase in check fraud. In 2023, $400 million in losses resulted from FI’s inability to verify legitimate mobile check deposits (RDC). However, transactions are not the only way to catch fraud after an account has been opened. 

The warning signs of impending fraud can be detected during the loan application process and are often more subtle and can appear months or years after an account is opened. This is a common pattern of what’s known as bust-out fraud, where an account holder exhibits normal behavior for a period of time and suddenly applies for loans or maxes out their credit cards with no intent of repaying them. While it may seem impossible to spot these bad actors before they strike, there are subtle behaviors machine learning (ML) models can use to detect fraudsters and prevent fraud loss.

Myth 3: Preventing fraud requires adding more time to the application review process and friction for borrowers. 

While some lenders resort to adding more application fields or limiting automation to catch fraud, modern technology offers a way to optimize fraud detection and automation. Fintech solutions like Plaid and Clutch use KYC (know your customer) workflows to help ease the burden of detecting fraud for lenders without adding more friction for borrowers. Advanced machine learning (ML) models are also becoming more common in the fight against fraud. These models can predict whether an applicant is high risk in seconds versus the hours or days it can take to verify the information manually. 

Ultimately, an FI’s risk tolerance will determine its policies and processes, but there is a world where lenders can have a safe, fast, and borrower-friendly application process.

Myth 4: Many fraudulent applications can be caught with manual reviews.

Even the most experienced underwriter can be outwitted by AI-generated deepfakes and documentation during a manual review. The tools used to create fraudulent documents are easily accessible online and likely contribute to the increase in fraudulent activity, according to FinCEN. However, lenders need to look out for more than just false documentation. AI-generated audio and video can be created in real time, adding to the complexity of identifying a fraudster. 

Fortunately, the same technology can be used to combat fraud. Manual reviews are prone to human error, and overlooking even the smallest detail can result in significant loss for a lender.  However, in the fight against AI fraud, AI technology can help level the playing field by detecting irregularities in an applicant’s IP address, reported income, contact details, and more. Unlike humans, these ML models can be retrained quickly on the newest fraud trends to ensure continued accuracy and protection against evolving fraud.

Myth 5: All fraud detection solutions provide the same results. 

Not all fraud solutions are the same, but there are common points of failure across many of them. Binary outputs, high false positives, and one-size-fits-all solutions are common traits that leave lenders in the dark and increase the need for manual reviews. Additionally, fraud management vendors often only address certain fraud types, resulting in a patchwork of decentralized point solutions that still leave gaps in detection for lenders.

As fraud tactics get more sophisticated, the game-changer for lenders is AI. Incorporating AI into fraud detection is closer to being a standard practice than you might think. A 2023 LexisNexis report showed that 52% of U.S. lenders listed “AI/ML models“ as one of the most important features of a fraud management solution. Unlike many solutions on the market, AI technology is more accurate, customizable, and agile to address the evolving fraud landscape. 

Conclusion

Fraud isn’t new and it’s certainly not going anywhere anytime soon. Advances in technology, including generative AI, will make detection even more difficult without proper solutions in place. Machine learning models are the best hope FIs of all sizes have in their fight against fraud. Putting systems in place today prevents the losses of tomorrow.