From Prediction to Recovery: Two AI Use Cases in Collections

How Qualco ML Studio improves allocation and supports automated legal eligibility decisions. 

For collections teams, recovery performance depends on more than the volume of accounts they manage. It also depends on the decisions made about each one: which agency should handle it, whether legal action is justified, and where specialist attention is needed.

The stakes are significant. More than €20 billion in consumer credit entered non-performing status across EU/EEA banks in the first half of 2025 alone (European Banking Authority, 2025). At the same time, AI is gaining ground in collections. BCG's 2025 research reports 26% adoption of AI in debt collection and recovery among surveyed financial institutions, including predictive analytics for delinquency management and repayment strategies.

The challenge is using that data to make better case-level decisions, rather than relying on fixed rules, historical averages and manual reviews. Qualco ML Studio addresses this through predictive analytics and AI-assisted machine learning. As the intelligence layer within Qualco Technology's Data, ML & Agentic AI Suite, it develops models that help financial institutions predict outcomes and inform treatment decisions.

The Suite brings together three modular solutions:

• Qualco Data Studio for data preparation
• Qualco ML Studio for predictive modelling
• Qualco Agentic Studio for applying model outputs to operational workflows.

Each can operate independently or alongside the others, integrating with existing systems.

Let’s explore two use cases illustrating what this means for collections and recoveries teams.

Use Case #1: Optimising DCA Panel Management

€2.31M in additional monthly recoveries. A 313% increase in collections. An 81% increase in payer rate. These were the results of an AI-supported allocation strategy applied to a portfolio of 197,000 cases across seven debt collection agencies (DCAs). Rather than relying on empirical allocation rules, the approach used predictive models to assess recovery potential for different case–agency combinations.

Why traditional allocation falls short 

Allocating cases across DCAs involves more than distributing workloads. Agencies differ in expertise, performance and capacity, while customer characteristics influence the likelihood of repayment. Yet conventional allocation strategies often rely on historical performance or fixed rules that overlook these differences, potentially limiting recoveries.

How predictive modelling improves allocation

The process follows three steps:

1. Qualco Data Studio prepares customer profiles, payment histories, agency performance data and capacity constraints.
2. Qualco ML Studio predicts payment probability and expected recovery for different customer–DCA combinations, assessing which agencies offer the greatest recovery potential for each case.
3. Qualco Agentic Studio applies business rules and capacity constraints to select the best-fit DCA and determine the allocation strategy.

In the client application, monthly recoveries increased from €0.74M under empirical allocation to €3.05M with full optimisation. Even at 60% optimisation, recoveries reached €2.20M per month.

Use Case #2: Automating Legal Eligibility Decisioning 

Clear-cut legal cases can progress automatically, while complex cases remain subject to human review. For legal recoveries teams, this means less time spent on routine assessments and more capacity for cases requiring specialist judgment.

Where manual assessments create delays

Before initiating legal action, teams must establish whether a case is legally ready, whether the expected recovery justifies the cost and whether related cases could be consolidated. When these checks are performed manually, straightforward cases can remain in review queues alongside those requiring more detailed assessment. Across large portfolios, this creates unnecessary processing work and can delay case progression.

How predictive intelligence supports legal decisions

The Data, ML & Agentic AI Suite connects eligibility assessment with subsequent case management:

1. Qualco Data Studio prepares case, customer and debt information, including linked cases that may enter the same legal process.
2. Qualco ML Studio evaluates legal readiness, cost efficiency and consolidation potential, helping determine which cases are suitable for legal treatment.
3. Qualco Agentic Studio applies business rules, routes exceptions for review and triggers the relevant actions in Qualco Collections & Recoveries (QCR), including process initiation and stage transitions.

Clear-cut, cost-effective cases can progress automatically to Legal Treatment, while complex cases are escalated for review. Eligibility, cost and consolidation checks are applied consistently, with decisions and subsequent actions traceable in QCR.

Beyond Collections: What's Next for Predictive AI?

The applications of predictive modelling extend well beyond DCA allocation and legal eligibility. From credit risk and fraud detection to customer segmentation, financial institutions face many decisions where more accurate predictions can inform their next move.

With AI-assisted model development, 100+ pre-built financial scenarios and full model lifecycle management, Qualco ML Studio helps teams build and deploy predictive models across financial operations, independently or alongside their existing systems.

The next opportunity could be within your own portfolio.

Ready to explore where Qualco ML Studio could make a difference to your operations?