Case Study: Enriching first-party data and features to better understand new to credit prospects and improve risk decisioning

Helping a digital loan provider fill in missing consumer credit information with highly predictive features and attributes

In Asia, where unbanked and underbanked populations are high, determining the level of risk and creditworthiness is rendered particularly challenging due to lack of credit history. It also takes time for new-to-credit customers to build a history and earn a standard credit score. Without access to such data, lenders are unable to confidently provide fast approvals and rapidly disburse loans.

When an all-digital instant loan provider faced this situation, they turned to Mobilewalla for assistance. Download this case study to learn more about:

  • AI-driven data and features at scale to enhance their understanding of new-to-credit prospects and build a more accurate profile of borrowers
  • The predictive modelling strategy used to improve risk decisioning without increasing overall loan default risk
  • How these tools reduced the lender’s portfolio risk by 15% and positively impacted their new-to-credit and thin-file loan origination process

Fill out the form to access the case study and learn how data enrichment and predictive modeling can benefit your business.

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