ML-driven scoring to prioritize collections efforts, predict payment likelihood, and optimize recovery strategies.
Not all delinquent accounts are equal. Our propensity to pay models identify which customers are most likely to pay, when they're likely to pay, and what contact strategy will be most effective.
By focusing collection efforts on accounts with the highest recovery potential, you can dramatically improve recovery rates while reducing operational costs and customer friction.
Machine learning models that predict the likelihood of payment within specific timeframes, enabling precise prioritisation of collection activities.
Dynamic ranking algorithms that continuously update account priority based on payment probability, balance, and expected recovery value.
Channel and timing optimization that determines the best way to reach each customer - SMS, email, call, or letter - and when to make contact.
Treatment assignment models that match customers with the most effective collection strategy, maximising overall portfolio recovery.
Built-in A/B testing frameworks to continuously validate and improve model performance against control strategies.
API-based scoring services that provide instant propensity scores, enabling dynamic workflow adjustments and real-time decisioning.
Let's discuss how propensity scoring can transform your recovery performance.
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