Mitigating Application Fraud Risk for Online Lenders

Reduce costs and provide better services by cutting down on fraudulent applications
The online lending industry has expanded exponentially in the past decade as the technology that supports it has improved and as consumers look for easier ways to access funds. The industry has seen massive gains, but it has also experienced some severe growing pains.
The problem

While many online lenders have turned to machine learning tools to help detect fraudulent applications, their reliance on internal data exclusively limits their efficiency. Our customer was using a rules-based approach for detecting potential application fraud, which works when the flow of applications is relatively low or stable but is easily overwhelmed. As a result, the company was losing almost $3 dollars for every $1 dollar tied to a fraudulent application, and nearly a quarter of its charge-offs were tied to application fraud.

The solution

Our customer connected their datasets to Explorium, and the results were immediate. Thanks to their new, enriched datasets, the lender generated new features for a model that could dynamically detect fraud, including: 

  • The likelihood an applicant is a real person based on internet activity and interactions online
  • Alternative risk scores based on a variety of economic and financial health data that indicate fraud risk
  • Internet and social media behavior that can point to previous fraudulent activity
  • Social media data that can pinpoint synthetic identities
  • Economic information that offers person-level context on a borrower’s previous loan repayment history, economic stability, and likelihood to repay
The results:

Smarter detection for reduced costs and higher accuracy

By connecting to Explorium’s Enrichment Catalog, the lender was able to more precisely determine fraudulent applications and improve their risk assessment, lowering their lending costs and charge-offs by 15%, and boosting their fraud detection rate on applications to 92% of all incoming loan applications before moving on from the initial form submission.

Reduce costs and detect fraudulent loan applications using augmented data discovery.

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