Lenders urged to act amid increased fraud

Equipment finance lenders are being strongly advised to adopt enhanced security measures aided by technology to combat an uptick in fraud. 

More than 80% of 135 surveyed professionals in the financial services industry reported a nearly 14% increase in small– and medium-sized business lending fraud over the past year, according to a study by LexisNexis Risk Solutions released Sept. 5. SMBs are defined as businesses earning up to $10 million annually. 

In the equipment finance sector, fraud such as identity theft, use of legitimate credentials by criminal enterprises and impersonation rose by 10% or more between 2022 and 2023, according to a January report by the Equipment Leasing and Finance Association. Among 30 surveyed equipment finance professionals, 43% said they were either somewhat or not at all confident in their ability to detect fake identities. 

In light of increasingly sophisticated fraudsters, LexisNexis recommended four measures to prevent SMB lending fraud. 

1. Enhance identity verification 

Identity verification is a crucial first step in preventing fraud. New technology can help lenders authenticate borrowers’ identities, especially since more applications are being submitted online rather than in-person, according to the report. 

Some equipment financiers are exploring AI-driven technologies to prevent stolen identities, Jay Darden, regional sales manager at Mount Joy, Pa.-based Farm Credit Express, told Equipment Finance News. 

“We are definitely seeing more sophisticated identity theft,” he said. “We are actively looking for technology to help us mitigate that risk, and that will never change.” 

Darden said Farm Credit Express has considered using an AI-driven model called TruePic, an app that geo-stamps a person’s location and traces the origins of a photo’s source as a means for identity authentication. 

2. Multilayered approach 

LexisNexis recommends a multi–layered approach to account for unique risks associated with different lending channels, payment methods and products. This approach includes two-factor authentication, biometrics and behavioral biometrics. 

Behavioral biometrics uses machine learning and AI to analyze behavior such as mouse movement, typing speed, page navigation and other ways in which users interact with a device. Linear mouse movements and high copy/paste volume are some abnormal behaviors that can identify potential fraudsters, according to the ELFA report. 

3. Early detection 

Lenders are being urged to focus on detecting fraud as early as possible. Identifying suspicious Social Security numbers or tax identification numbers can weed out fraudsters from the start, according to the report. 

Early detection is another area that calls for AI adoption, Hemant Madaan, chief executive at Richardson, Texas-based software provider JumpGrowth, said during a webinar on AI-driven finance last month. 

“For example, we can now scan the documents and automatically check if the document is a good contender for fraud or things like that,” Madaan said at the time. “So, that’s being done using patent recognition already.”  

4. Share intelligence 

LexisNexis recommends that financial institutions use collective intelligence, sharing information through consortiums and digital identity networks. This can create a “peer-based intelligence layer” that secures digital channels and bolsters real-time risk decisions.