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November 10, 2021 - Parinitha Marnekar

Practical Guide to Stop Loan Stacking

Let’s start with a one-line explanation of loan stacking. Loan stacking occurs when someone takes out multiple loans (from multiple lenders) within a short period of time to take advantage of delays in credit reporting processes. 

For a full definition of this practice, check out this quick summary.

Legitimate instances of loan stacking exist. For example, a borrower could apply for several loans looking to compare credit rates and other contract terms before accepting one of them. However, lenders need to be alert because as this TransUnion report notes, stacked loans are four times as likely to be fraudulent as single ones. Why? Fraudsters can maximize unlawful profits by taking out several loans before they appear on credit reports. This is known as a bust-out fraud attack

Why Traditional Fraud Systems Fail to Catch Loan Stacking

Fraudsters often get away with loan stacking because they use stolen or synthetic identities to submit applications. Many of the pieces of information they use to take out loans are legitimate on the surface. But when put together like a jigsaw puzzle, the pieces don’t fit, don’t create the right picture, or are partially missing. 

For example, a loan application might have a person’s name, address, and social security number, but the phone number might belong to a different country. This could signal that an attacker might be trying to commit identity fraud, using a cell phone in his control to bypass 2-factor authentication measures. As this example illustrates, adding more authentication measures to the application process increases customer friction and will not necessarily solve the problem. 

Best Practices for Identifying Loan Stacking

The true solution lies in fraud detection solutions designed to look holistically at the data generated by the application process implemented in tandem with the following best practices:

  1. Look Into Alternative Data Sources

Don’t dismiss credit record information; it’s an important source of information. Instead, complement it with alternative information sources such as rental records, utility bills, and address history to get a more detailed view of a person’s financial history to enhance their customer risk evaluation and analyze discrepancies to decide whether or not to conduct a deeper fraud investigation before disbursing the funds. 

  1. Know your customer, their industry, and their market and underwrite appropriately.

Whether it’s personal or business loans, the efforts you put into investigating and understanding the underlying forces and dynamics of your target market will pay off. A more thorough comprehension of your borrowers will enable you to spot details in credit applications that do not make sense and raise the appropriate flags in due time.

  1. Design your application process striking a balance between frictionless customer experience and prudent risk assessment. 

Advanced fraud detection strategies should enable you to highlight good borrowers with a high degree of certainty and speed them through the application process to ensure the delivery of a great customer experience. The other—less trustworthy—applications could well warrant additional review instances. With the right tools, this review can be made substantially easier.

Invest in Real-Time Financial Fraud Prevention Tools

Strong financial fraud prevention tools work in real-time to speed up decision-making and minimize friction for good actors. The sooner loan stacking can be discovered, the sooner lenders can take action to mitigate its financial impact. The results achieved by combining identity data, advanced machine learning, and high-impact fraud visualization and decision tools will exceed your expectations. 

Did you know that online lenders have achieved 5x improvements in review efficiency and 95% accuracy with <1% false positives with DataVisor? Here are all the ways in which we can help you stop application fraud once and for all.

Still curious? Check out this case study where a lender that revolutionized the POS lending space squashed fraud and simultaneously improved the customer experience.

Consumer Lender Raises the Bar for Customer Experience and Reduces Fraud with Machine Learning

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