Case Study: a mobile network operator achieves automated credit scoring and lending decisions with Zoral

Mobile Network Operator - Customer Case Study

An emerging market mobile network operator (MNO) needed to launch a mobile financial services (MFS) lending platform but lacked access to traditional credit data sources. The challenge was to determine if their own MNO data alone could be used to accurately predict customer credit risk and default probability to support a profitable micro-lending product. They engaged Zoral to assess this possibility and build an automated underwriting solution.

Zoral implemented its platform, including its Analytical Data Workbench and Decision Engine, to cleanse and analyze the MNO's data. They tuned and blended multiple machine learning models, achieving excellent predictive results with AUC scores between 0.78 and 0.91. This allowed the MNO to segment customers by risk and deploy a fully functional digital lending MVP, including credit scoring, limit management, and fraud detection, within just eight weeks of receiving the data.