By Manny Plasencia, TransUnion
At 9:12 a.m., a third-party collector who’s calling a delinquent account hears the hesitation on the line: “I’m behind because my hours got cut. What can we do?”
In that moment, the challenge is bigger than a single call. The agent is balancing empathy, compliance, client expectations and recovery performance — all while representing both the agency and the creditor’s brand. In third-party collections, those 60 seconds can shape liquidation outcomes, audit exposure and customer trust.
That’s where responsible AI moves from concept to operational value.
When deployed effectively, AI can help collections organizations reduce costs, improve recoveries and strengthen customer experiences. McKinsey reports advanced generative AI in credit customer assistance and collections can reduce operational expenses by up to 40%, improve recoveries by about 10%, and increase customer satisfaction by as much as 30%.1 This means agencies can translate AI investments into measurable gains in efficiency, recovery performance and customer experience rather than incremental process improvements.
For third-party collections, however, technology alone isn’t enough. Real results depend on combining AI with trusted data, explainable analytics and governance that can stand up to client scrutiny and regulatory expectations. That’s where TransUnion® plays a critical role — helping you turn better data into better decisions across the collections workflow.
How TransUnion adds value in third-part collections strategies
In third-party collections, responsible AI is only as strong as the data informing it. TransUnion helps strengthen that foundation with credit, identity and analytics capabilities.
That matters because you’re not managing one static process. You’re navigating multiple creditor programs, changing account populations, consumer contact challenges and overlapping compliance requirements. Better data helps AI become more than a scripting tool; it becomes a decision support layer that can guide actions in ways that are more relevant, consistent and measurable. The result is a more reliable decisioning foundation that helps agencies improve liquidation rates while maintaining consistency across client programs and compliance expectations.
Six steps to apply responsible AI in third-party collections
- Reduce ramp time across portfolios
Third-party collectors rarely learn a single playbook. They must absorb different client rules, disclosure requirements and treatment strategies quickly.
Responsible AI can shorten that learning curve by delivering real-time guidance tied to the account, conversation stage and applicable client workflow. When paired with TransUnion data and analytics, that guidance becomes more context-aware, helping agents understand not just what to say but what to do next for the account in front of them. This enables agencies to onboard faster, reduce early-stage errors and reach productivity benchmarks sooner across diverse client portfolios.
- Improve consistency without losing the human element
Consistency in third-party collections isn’t about making every outbound conversation sound the same. It’s about ensuring every consumer receives accurate information, fair treatment and an experience aligned with both policy and brand expectations.
AI can reinforce approved talk tracks and escalation paths and offer logic in real time — while TransUnion data can help you segment accounts more intelligently and align outreach strategies more closely to risk and customer circumstances. Use this approach to support consistency at scale without reducing agents to scripts. This approach helps agencies deliver more uniform outcomes, protect client brand expectations and reduce variability that can impact compliance and recovery rates.
- Strengthen compliance in real time during collections calls
In a regulated environment, post-call quality review is often too late. You need guardrails that work during the interaction, not after it.
Responsible AI can flag missed disclosures, risky phrases and escalation triggers as the conversation unfolds. Build those guardrails on governed data and clear rules so you can explain, monitor and improve over time — an important advantage for agencies that must demonstrate control to clients and regulators alike. This reduces avoidable compliance risk, lowers remediation costs and enhances an agency’s ability to demonstrate control during client and regulatory reviews.
- Give collections supervisors earlier performance and risk signals
Supervisors can’t review every interaction, but you can help them act faster by showing where risk and performance issues are emerging. That’s especially important in third-party collections where client scorecards and service levels leave little room for delayed intervention.
AI-generated summaries, trend detection and performance signals can help managers focus coaching where it will have the most impact. Strengthen those signals with better data analytics so you can move from reactive oversight to more targeted operational management. This allows supervisors to intervene earlier, facilitate more efficient agent performance and protect service levels tied directly to client scorecards.
- Turn collections documentation into actionable intelligence
After-call work is a major drag on productivity, but documentation is essential in third-party collections. You need complete records not only for internal operations but also for audits, disputes and client reporting.
AI-generated transcripts and summaries can reduce manual effort and create a more consistent audit trail. TransUnion’s role is to help connect those interaction outputs to broader analytics and decisioning frameworks — so information captured during the call can also improve future strategy. This augments operational efficiency while creating a more defensible audit trail and enabling better-informed strategy adjustments over time.
- Move from reactive debt recovery to data-driven outreach strategies
The most strategic value emerges when AI and data are used not only to support the current interaction but to improve what happens next. Focus on choosing better channels, better timing and better treatment approaches across portfolios.
Use more effective identity resolution, credit insights and analytics to prioritize accounts, tailor outreach and refine recovery strategies with greater confidence. In this model, responsible AI isn’t just an efficiency tool; it’s a way to make third-party collections more targeted, compliant and effective. This helps agencies increase right-party contacts, improve treatment effectiveness and drive stronger recovery outcomes with fewer unnecessary touches.
How to measure ROI from AI
For agencies and creditor clients alike, the ROI case becomes stronger when it’s transparent. Time savings from reduced after-call work, improved recoveries and fewer compliance breakdowns are easier to defend when underlying workflows are explainable and data inputs are trusted. (Check out this case study to see how it plays out in real life.)
Responsible AI doesn’t create value in isolation. It creates value when it’s grounded in high-quality data, applied through governed analytics and aligned to the realities of third-party collections operations. This clarity makes it easier for agencies and their clients to align on value, justify continued investment and scale AI initiatives with confidence.
Turn collections strategies into real-time decisions
Responsible AI in third-party collections doesn’t live in a policy document. It shows up the moment an agent needs to make the right decision quickly, compliantly and with empathy. This positions agencies to compete more effectively by turning everyday interactions into opportunities for better outcomes across performance, compliance and customer experience.
TransUnion’s role at that moment is to help make smarter decisions by bringing together the data, analytics and intelligence needed to turn responsible AI into measurable operational and financial results. Learn more about our solutions for third-party collections.
For more information on the state of the industry, explore the results in the Debt Collections Report.
1 McKinsey and Company: The promise of generative AI for credit customer assistance