BANKING FRAUD DETECTION & PREVENTION: TIPS FOR LENDERS

Lenders should take a look at how much lending fraud has cost them recently. According to a study by LexisNexis Risk Solutions, the cost of fraud rose, as US financial services firms saw that for each $1 of fraud that occurred, a $4.36 increase in costs was incurred in 2022, up from $3.64 in 2020. Meanwhile, nearly a third of all fraud costs in 2022 was due to mobile banking, up from 27 percent in 2021. 

The threat of auto loan fraud is also increasing and, in many cases, has become a new and preferred form of auto theft. The auto lending industry faced over $8.1 billion in origination risk exposure in 2022, a $400 million increase compared to 2021, according to Point Predictive. The volume and size of fraudulent loans issued by lenders are driving actual losses for lenders. With easy access to online resources, it’s never been simpler to commit banking fraud. This has made banking fraud detection a necessary aspect of the loan origination process.

Most Frequently Encountered Fraud Types
Identity	When fraudsters use a stolen or synthetic identity composed of seemingly legitimate personal information and credit data.
Straw borrower	An applicant with poor credit who convinces another person to be a front for purchasing a vehicle. This may be a friend or relative with better credit or done by a fraud ring to acquire vehicles for sale in foreign markets.
Income	When fraudsters intentionally misrepresent the existence, continuance, source, or amount of income. This often includes false pay stubs, which are easily created online for a small fee.
Employment	Using fee-based online services, fraudsters confirm false employment via phone, mail, or electronic communications.
Undisclosed debt	Failure to disclose all current real estate debt or past foreclosures.
Property value	Intentionally misrepresented information about the value of property being purchased for the sake of improving loan terms.
Collateral inflation	When auto dealers claim a higher value for a used vehicle than the actual sales price in order to boost profits.

Banking Fraud Detection Requires the Latest Fintech Innovations

Banks can counter any damage done by fraudulent loan applications by using the latest innovations in financial technology (fintech), such as:

• Secure, on-demand access to consumer data for better lending decisions.
• Machine learning algorithms for automated banking fraud detection.
• Cloud-based verification services to confirm or refute claims made on loan applications.

Consumer Data Provides a More Accurate Assessment of the Ability to Pay

Fostered by the internet, an ever-growing volume of consumer data is becoming available to banks so that they can better assess the financial standing of loan applicants. Automated calls to alternative credit data sources can be used along with traditional credit scores to provide a more detailed and current assessment of applicants’ ability to pay. Data on late rental, utility, and cell phone payments, along with payday loans, frequent changes of address, and discrepancies between educational records and employment often correlate with higher-risk loans. This calls for a more thorough vetting of applicants.

• Secure, on-demand access to consumer data for better lending decisions.
• Machine learning algorithms for automated banking fraud detection.
• Cloud-based verification services to confirm or refute claims made on loan applications.

Consumer Data Provides a More Accurate Assessment of the Ability to Pay

Fostered by the internet, an ever-growing volume of consumer data is becoming available to banks so that they can better assess the financial standing of loan applicants. Automated calls to alternative credit data sources can be used along with traditional credit scores to provide a more detailed and current assessment of applicants’ ability to pay. Data on late rental, utility, and cell phone payments, along with payday loans, frequent changes of address, and discrepancies between educational records and employment often correlate with higher-risk loans. This calls for a more thorough vetting of applicants.

Machine Learning Uncovers Multiple Fraud Schemes

A historical analysis of tens of millions of auto loan applications reveals both subtle and obvious schemes involving the misrepresentation of information on loan applications. Machine learning algorithms can be applied to evaluate thousands of loan applications quickly, and this technology is considered one of the most effective innovations in banking fraud detection. This automated process can help detect fraudulent activities like false and synthetic identities, straw buyers, income and employment misrepresentation, and collateral inflation far more efficiently than a manual review. While machine learning technology won’t replace the most experienced underwriters, it will optimize the process of uncovering fraudulent schemes.

The Convenience of Verification Services 

When machine learning algorithms detect potential fraud, automated calls to income, employment, and vehicle valuation services help confirm or refute the suspected fraud. Such confirmation eliminates these problematic applications while allowing for further review of applications that contain unintentional errors. Refutation of fraudulent applications also improves lending decision confidence in structuring the appropriate lending terms.

Fintech Innovations Address a Wide Range of Fraud Schemes

Though the chart below isn’t comprehensive, it provides indications as to what types of technology can counter which types of fraud.

Type of Fraud	Alternative Credit Data	Machine Learning Algorithms	Verification Services
Identity		⚫
Straw Borrower		⚫
Income		⚫	⚫
Employment		⚫	⚫
Financial Strength	⚫
Property Value	⚫
Collateral		⚫	⚫

The Vital Role of Automation in Banking Fraud Detection 

Each one of these fintech capabilities and services described above uses automation to make banking fraud detection as efficient and effective as possible. Using cloud-based services, modern loan origination systems use them as an integral part of the underwriting process. These automated origination tools immediately call attention to suspect loan applications. They provide comparisons between what applicants put on their applications and their actual financial attributes, giving lenders a means to better classify specific applicants as either legitimate or suspicious. Such analyses provide banks with a means to lower their risk exposure.

Proactively Get Ahead of Fraud

Although fraud statistics often show conflicting trends, it’s clear that bad actors will continue to use technology to their benefit. To limit the damage from potentially fraudulent loan applications, banks and other lenders need to have the latest fintech capabilities at their disposal. Without such innovations, lenders should expect their losses from fraud to rise. Proactive deployment of these technologies for banking fraud detection will provide both immediate and long-term benefits for a lender’s loan portfolios.