PayPulse
Self-learning ML ensemble that predicts which accounts will pay
- Service
- AI Adoption & Automation
- Sector
- Healthcare & Revenue Cycle
- Built with
- Python
- SQL Server
- XGBoost
- LightGBM
- CatBoost
- RandomForest
- scikit-learn
- pandas
- NumPy
- MLflow
The constraint
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.
Outcomes
- 0187% accuracy vs previous solution's 62%
- 02Scores thousands of accounts daily
- 03Self-learning feedback loop retrains weekly
- 04Collection agents prioritize high-probability accounts
- Prediction accuracy
- 62% → 87%
- Accounts scored
- Daily
- Automated retraining cadence
- Weekly
Tell us what the process costs you today.
That number decides whether any of this is worth building. Bring it to the call and we can get to a straight answer inside half an hour.
- Emailsupport@eletech.io
- RepliesWithin one working day
30 min · no charge
Pick a time and we’ll talk it through.
- 30 min, and no charge for it
- Bring the process and what it costs you today
- A straight answer — including “don’t build this”