Understand How Fintech Companies Use Third-Party Data to Prevent Fraud & Decrease Defaults

In areas where credit scores are not widely used, finserv and fintech companies typically base decisions to extend credit on information collected directly from the applicant. Unfortunately, this limited data is frequently insufficient for preventing fraud and filtering out likely defaulters.  

When one all-digital fintech company faced this situation, they turned to Mobilewalla for assistance. Read the case study to learn more about:

• The predictive modeling strategy used to predict defaulters more accurately
• Readily available predefined data features found to be most predictive of credit risk
• How these tools resulted in a 5% Gini Index lift and decreased default rates

In fintech, even small improvements in risk modeling can translate into big increases in revenue. Please fill out the form to access the case study and learn how data enrichment can benefit your business.

Fill out the form to access the case study and learn how data enrichment and feature engineering can improve your predictive modeling results.