Use Case
Optimising Debt Collection Agency Allocation
From empirical rules to €3.05M in monthly gains: Intelligently assign legal cases to the most appropriate DCA, maximising total profits and achieving a recovery uplift of €2.31M per month.
Challenge
How AI can help
The QUALCO Data-Driven Decisions Engine (D3E) replaces static decision-making with algorithmic precision. By analysing debtor-level data, payment patterns, and DCA-specific performance, D3E identifies the optimal DCA for each case—improving outcomes while preserving operational stability.
AI Model Development
Two AI predictive models are trained per debtor–DCA pair: a classification one to predict payment likelihood and a regression one to estimate expected recovery value.
Prediction & Allocation Calculation
D3E combines these outputs to forecast recovery per pairing, accounting for DCA-specific constraints such as capacity limits.
Optimised Assignment Strategy
An optimiser algorithmically computes the most profitable and feasible allocation strategy across the entire portfolio by analysing a broad spectrum of assignment possibilities.
What to Expect
Significant Profit Increase
Monthly collections increased from €0.74M to €3.05M – a 313% uplift.
Improved Resource Allocation
68% of cases were reallocated to DCAs, aligned with operational capacity, so that total revenue is maximised.
Enhanced Payer Outcomes
Payer rate increased by 81%; average payment per debtor more than doubled (2.3× gain).
Low Implementation Cost
Proof of Value was completed at just €0.13M per year.
Minimal Operational Impact
D3E integrated seamlessly, requiring minimal client involvement and causing no disruption to existing operations.