Fraud x PDL

A fintech vendor in the commercial lending space came to People Data Labs with a problem. While their revenue depends on their ability to verify the credibility and history of potential credit applicants, they struggled to find sufficient data to confirm applicants’ identities. This problem was particularly acute around small businesses which often have sparse credit histories. Operators may not have an easy way to find the applicant associated with the business. Historically, these businesses were frequently denied loans due to their lack of credit history resulting in lost opportunities for small business applicants and lost revenue for vendors.

The global cost of fraud to businesses is estimated to be over $3.7 trillion dollars per year. For enterprise businesses in the commercial credit and lending space, this cost represents an existential threat, not only to revenue, but to their ability to provide lending services to business clients. These loans are especially vital to small businesses and startups which may need access to credit in order to launch, scale, and thrive. But those same small businesses are inherently riskier to lenders.

The vendor also struggled to address the challenge of understanding applicants’ work history in a world where new businesses are emerging every day making it increasingly challenging to verify real employment history and business ownership experience. The United States issued over 800,000 new business tax IDs in 2020, up from 770,000 the previous year. Many of these were launched online and eschewed standard identifiers like a DUNS number– until recently, the gold standard for concretely identifying a business name and tying that name to a legitimate tax entity. Without this link, it becomes increasingly hard to separate those with real experience at legitimate businesses from fraudsters who misrepresent themselves.

People Data Labs worked with the enterprise vendor across a number of products to help them improve their verification and fraud detection capabilities. Using PDL data through the Enrichment API, the vendor was able to enrich the record of potential credit applicants with relevant B2B data in order to more easily connect the applicant’s name and credit application with their work history and professional background. Enrichment allowed the vendor to save time by reducing false-positive matches from individuals with the same name or similar profiles and to more quickly confirm whether applicants had accurately represented their work history and creditworthiness.

In the second phase of the partnership between People Data Labs and the credit vendor, we piloted a program using aggregations around PDL’s person and company data to link individual applicants with firmographic data that could concretely link the business on their resume to real companies in order to more efficiently substantiate their work history. PDL’s ability to link individual person data to fresh and accurate B2B data through our unique APIs proved a perfect match for the challenges facing commercial credit vendors in this time of accelerating online and offline entrepreneurship.

Faster Matches: Business lending is a time-sensitive operation. Small business owners work with narrow margins and tight turnaround times and delays can be harmful to both the stability of their business and the completion of the loan. By enriching applicant records, the vendor was able to accelerate approvals by matching applicants with their personal and professional history and screening out other individuals with similar names and profiles.

Better Business Matches: Using PDL’s company data linked to person data, the vendor was able to link applicants to businesses on their resume. This allowed the vendor to quickly and efficiently identify applicants with legitimate work and operational history, to screen out those who might be misrepresenting their relationship with a legitimate business, and to filter out those whose history contained an illegitimate or non-existent business.

More Accurate Fraud Models: Using PDL’s data, the vendor was able to increase the accuracy of its fraud detection models overall, allowing it to prevent more losses due to fraudulent or unqualified applications. While we can’t reveal exact metrics, even small increases in efficiency can help preserve significant value. Preventing just one percent of total global fraud losses would allow credit vendors to recapture $37 billion annually