This is like giving your collections team a smart weather forecast for each loan: instead of treating all late payers the same, the system predicts how likely each customer is to pay at every stage of delinquency, so you can decide who to call, who to email, and where to focus effort for the best return.
Traditional collections treat many delinquent accounts similarly, leading to wasted effort on low‑propensity payers and missed opportunities on customers who would respond well to cheaper, lighter‑touch actions. This work compares machine learning methods to predict the probability of payment at different arrears stages so that banks can optimize strategies (who to contact, how, and when) and reduce losses and operating costs.
Proprietary borrower and behavioral data combined with institution-specific segmentation and policy rules; integration of models into operational collections workflows and decision engines creates switching costs and process stickiness.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Model performance and stability across economic cycles and portfolio shifts; data quality and feature drift over time.
Early Majority
This research focuses specifically on comparing multiple ML techniques for predicting payment probability at distinct arrears stages, enabling stage-specific strategy optimization, rather than a single generic risk or scorecard model across the entire collections lifecycle.
146 use cases in this application