Managing risk in consumer lending and finance is complex, especially when working with nonprime consumers, many of which have little to no credit history. Last month, in our webinar, “How to Grow Market Share After COVID-19: Best Practices for Nonprime Lending & Financing,” Abisht Raviprakash, Director of Portfolio Analytics for Enova International, covered the general components of an underwriting system for online consumer lending:
- Device Verification
- Identity Verification
- Bank Transactions Analysis
- Tradelines Analysis
- Competitive Landscape Analysis
Each component requires access to multiple 3rd-party data sources and integration of that data with 1st-party data. To help lenders manage the complexity of tradelines analysis, we partnered with Digital Matrix Systems to enable our clients quick and easy access to both credit bureau and alternative credit data. Leveraging credit bureau data in conjunction with alternative data sources allows lenders to get a clearer picture of creditworthiness of nonprime consumers as opposed to using a traditional credit score alone. Learn more about our strategic partnership with Digital Matrix Systems from our press release last month.
Along with our other strategic partners, Enova Decisions is able to offer our clients access to best-in-class data for every component of the underwriting system. Leveraging that data, we work with our clients to tailor advanced analytics, including machine learning techniques, to identify the most predictive data in optimizing key risk decisions. Use cases include:
- Auto-declining high-risk applicants due to likely fraud and/or poor creditworthiness
- Isolating medium-risk applicants for further remediation
- Pricing offers based on an applicant’s risk tier
As a result, lenders can make smarter fraud and credit decisions and profitably serve more nonprime consumers.
Why wait? Learn how you can optimize your fraud and credit decisions.
Contact us today to get started.