Overview
Delinquency collections traditionally rely on human advisors to review cases, navigate systems, ensure compliance, and make subjective judgments. While SOPs exist, each case requires customized action for a personalized engagement—making the function time-intensive, inconsistent, and risk-prone. We worked with Target, a Tech Mahindra FCA regulated entity, that supports global lending clients, to modernize their arrears management.
Client Challenges
While Target’s advisors need to review cases, interpret policies, validate data across 22 legacy systems, and apply rules, there were certain bottlenecks:
- High Manual Effort: 25–40 minutes per case
- Cognitive Load: Advisors track prior solutions manually
- Limited Scalability: Person-dependent processes
- Fragmented Tech Landscape: Multiple platforms, minimal automation
Our Approach and Solution
Collections Guru: An Agentic AI Solution
Tech Mahindra deployed an AI Agent trained on manuals and historic data, integrated into workflows for instant, accurate support. The solution offers agentic case review, applying rules autonomously and providing decision recommendations. It includes real-time advisor-assist, delivering immediate guidance for faster, more confident decisions. Through workflow integration, it retrieves inputs across systems to reduce unnecessary clicks. Its compliance-first design ensures adherence to FCA standards with full traceability. With deep legacy integration, it works seamlessly without requiring major platform overhauls. Finally, it is human-centric, reducing stress and enhancing usability throughout the process.
Reshaping Legacy Systems with Agentic AI
Target UK's transformation demonstrates the tangible value of Agentic AI in regulated financial services operations. The initiative delivered clear outcomes in efficiency, compliance, and advisor experience—while setting up a future-ready operating model.
Tech Mahindra’s solution proves that advanced AI can meaningfully reshape operations even in legacy-heavy, compliance-driven environments. The success of this pilot and early-phase rollout provides a strong foundation for multi-client scaling and broader enterprise adoption.
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