First Payment Insights Model

Across South Africa’s fast-moving lending market — subscription, micro, short- and long-term products — some applicants apply for credit with no intention of repaying. Unlike consumers who fall behind due to genuine financial strain, this segment looks for weaknesses in onboarding and verification, then exploits them to access services without accountability.

The resulting cost goes well beyond missed payments: It raises operational spend through added fraud investigation, reduces revenue, distorts portfolio performance metrics and weakens risk-adjusted profitability.

First Payment Insights Model is a machine-learning model — powered by TransUnion^® bureau data — that predicts first-payment default risk across industries. Using bureau data accessed through the regulated framework, it gives lenders the insights to refine approval strategies and reduce exposure to first-party fraud. The model draws on more than 50 variables and can be run independently, alongside account origination models or within fraud prevention.