PayPulse
Self-learning ML ensemble that predicts which accounts will pay
The problem
A debt collection company had a high-cost, low-accuracy solution to predict/prioritize which accounts/debtors they need to call from thousands of accounts, leading to wasted collection efforts on low-likelihood accounts.
What we built
Built a self-learning ML ensemble (XGBoost, LightGBM, RandomForest, CatBoost) trained on years of the client's historical account outcomes and demographic data, so scores reflect how similar accounts actually behaved. It scores every account daily with a propensity-to-pay probability, running on an automated SQL + Python pipeline with a feedback loop that retrains weekly.
Machine Learning & Predictive Analytics — architecture sketch
Results
- 87% accuracy vs previous solution's 62%
- Scores thousands of accounts daily
- Self-learning feedback loop retrains weekly
- Collection agents prioritize high-probability accounts
¹ How we measured: Accuracy measured as paying-account prediction rate over 5 months of production scoring, compared against the client's prior credit-rating-based system (62%).
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