Point Predictive is the only data science company to host a world-class fraud risk consortium. By gaining access to numerous insights that your team would not have access to on any public records, we can find and fight fraud together. And when you put the power of this data to work for your portfolio, you can identify more fraud, better. Here is a list of just some of those insights.
1. Identify Fraud Risk of Potentially Fake Employer Information
Fraud risk is present if the borrower or dealer uses a fictitious employer that has been detected at other lenders. We saw a 300% increase in employer related fraud in 2020.
2. Fraud Risk is Present If Your Borrower Recently Reported As Self-Employed But Switched
Find out if the borrower reported to be self-employed in the last 30 days, but now they report as a wage earner. In 2020, we discovered that 43% of borrowers claiming to be self-employed had a subsequent application reporting to now be a wage earner.
3. Yes, a Straw Buyer Represents Fraud Risk
Find out if the borrower is likely purchasing the vehicle for someone else. We can identify recent applications for the same borrower which may have had a previous co-borrower removed.
4. Identify If The Vehicle Price Increased Dramatically Recently
Find out if the vehicle being purchased is priced accordingly. Or if the dealer is attempting to “powerbook” the loan. Price inflation can present significant risk to the relationship between the lender and the dealership. For instance in 2020, we saw roughly 20% of applications where the vehicle was priced at least $1,000 higher than advertised.
5. Identify If Other Lenders Find The Dealer Risky
Find out if other lenders recently terminated the dealership. Or if they have high rates of fraud and default. The power of our consortium can identify dealership risk across lenders. As a result, you don’t have to fight fraud alone.
6. Identify If The Vehicle Make and Model Is Targeted by Fraudsters
Vehicle makes and models come with differing levels of risk. Whether reliability, cost to own, or popular among fraudsters. Gain an understanding of collateral risk patterns and fraud-ring risk.
7. Spot Borrowers Who Recently Changed Their Income Significantly
Ever wondered if an applicant inflated their income on an application? If a customer doesn’t qualify for the loan they want, they may try again with some adjusted numbers. Point Predictive collects and tracks discrepancies in application values across lenders to prevent income misrepresentation. A 2019 study in Canada by Equifax showed that 19% of surveyed millennials had not been entirely truthful on a credit or loan application.
8. Identify Borrowers Who Changed Their Employer Recently
Similar to income discrepancies, applicants may choose to show a different employer. This commonly occurs for self-employed borrowers who may find better luck listing the company name rather than self-employed when applying for loans.
9. Identify Potential Credit Washers
Coached by credit repair schemes, some borrowers will either create synthetic identities by using alternative identifiers or wash their credit. Only Point Predictive offers a credit washing detection capability. By looking for sudden swings in credit scores with a sudden drop in negative tradelines over a short time period.
10. 110 Unique Data Points Not Available On Bureau or Public Record Source
Point Predictive’s consortium leverages a unique approach in its method of collection as well as the data points aggregated across lenders. As a result, the information is highly effective and predictive alone. As well as greatly complementing existing tools and data sets in lenders’ arsenals against fraud and loss.