AI Platform Cuts Uncollected Cases by 5% in 4 Months for Debt Recovery Firm

Context

A private equity–backed debt recovery firm in the financial services sector needed to integrate three separate businesses (two of them recently acquired) onto a single AI-driven case management platform. The objective was to remove significant compliance risk around data privacy and reporting, improve revenue per client through better prioritisation of cases and analytics, and create a platform capable of scaling multiple service offerings with consistent workflows and measurable value for investors.

Challenges

Data across the three businesses was untrustworthy and siloed by business unit and service offering, preventing accurate reporting and consistent decision-making. All three businesses carried serious compliance risks related to data privacy, consent management and regulatory reporting. The organisation also lacked AI maturity: teams were unfamiliar with machine-assisted decision-making, and there was scepticism about embedding predictive models into operational workflows. Finally, investor pressure mounted as the hold period approached, creating urgency to demonstrate measurable value while keeping regulatory and reputational risk tightly controlled.

Implementation

The Fractional Head of AI and the programme team designed and launched a single enterprise data model and standardised workflows to serve as the core of the new case management platform. The team transformed data from all legacy business systems into the standard model in two months, consolidating customer records, contact histories and case states to create one trusted source of truth.

AI workstreams were reframed as “improving decision-making” and explicitly tied to operational KPIs such as propensity-to-pay accuracy, case prioritisation uplift, contact success rate and EBITDA contribution. The technology components were kept “under the hood” so frontline staff and managers focused only on the operational changes and measurable outcomes. Decision-support tools—like propensity-to-pay scoring and automated prioritisation—were designed to operate without exposing personal data: the data model was privacy-first, and predictive features were firewalled from raw identifiers. This ensured that models made recommendations based on pseudonymised or aggregated attributes while personally identifiable information remained protected.

Concurrently, the team established robust data governance and management processes: consent and access controls, lineage tracking, approved transformation rules, and an audit-ready reporting framework. These processes, overseen by the Fractional Head of AI, addressed regulatory reporting requirements and remediated privacy gaps within six months. To maintain investor confidence and sustain momentum under hold-period pressure, clear communications were prepared for the board and investors. The communications mapped the roadmap to key value targets (EBITDA improvement and expected exit multiple uplift) at each stage and regularly demonstrated value creation as milestones were achieved.

Results

Within four months of platform deployment, uncollected cases dropped by 5% driven by improved end-to-end visibility across the recovery process, more accurate contact data and smarter prioritisation of high-propensity accounts. The standardised data model enabled faster, more reliable reporting and removed previously hidden reconciliation errors, which supported operational decisions and investor reporting.

All identified compliance risks were mitigated within six months through the new governance framework, privacy-by-design data model and audited decisioning controls. Reframing AI as decision support and keeping models transparent to business users accelerated adoption despite low initial AI maturity, and the investor-facing roadmap helped preserve project momentum as the hold period neared its end.

Overall, the initiative delivered a repeatable platform that unified three businesses, reduced operational and compliance risk, produced an immediate measurable improvement in collections, and created a clear, investor-friendly line of sight from technical deliverables to EBITDA and exit multiple value creation.