The Challenge
A national credit bureau needed to move beyond basic data provision to offer a predictive scoring model but lacked the in-house expertise to develop and deploy ML at scale.
Our Solution
We built a complete ML pipeline from data preparation to production deployment, creating a bureau score that became the new industry standard.
Data Integration. Combined traditional credit data with alternative sources including telecom, utilities, and trade payment behaviour.
Feature Engineering. Developed over 200 predictive features, including payment velocity, stability indices, and utilisation patterns.
Model Development. Tested multiple algorithms (XGBoost, LightGBM, Logistic Regression) with rigorous validation and selected the optimal challenger.
Monitoring. Implemented a full monitoring framework, including drift detection (PSI), performance tracking, and automated retraining triggers.
Impact
The challenger delivered a 0.85 Gini coefficient — well clear of the 0.70–0.75 industry benchmark — and was adopted by every one of the 50+ financial institutions that received it, with sub-second scoring latency enabling real-time decisioning at the point of credit application.