Agentic AI Drives 60% Productivity Gains

~60%

Productivity and efficiency improvement

~70%

Reduction in customer complaints

Client Overview

Our client is a fast-growing US-based credit card issuer headquartered in the Midwest, specializing in near-prime and non-prime consumer segments. Managing a multi-billion-dollar credit portfolio, our client serves millions of active cardholders across the United States through retail partnerships and direct-to-consumer channels.

Operating in a highly regulated payments environment governed by CFPB and card network dispute mandates, our client processes a substantial volume of chargebacks and customer disputes annually across credit, billing, fraud, and identity-related categories. With dispute volumes rising in line with portfolio growth and digital transaction expansion, our client required a scalable and compliance-driven operating model.

As regulatory scrutiny intensified and customer expectations for faster resolutions increased, operational precision, response speed, and dispute accuracy became critical to protecting revenue, minimizing write-offs, and preserving customer trust.

The Challenge

Manual Disputes Handling: Limiting Speed, Accuracy, and Scalability

Our client faced mounting pressure to manage rising dispute volumes efficiently while maintaining strict compliance standards.

The existing process relied heavily on manual effort to retrieve disputes, categorize cases, extract relevant data, validate information, and prepare responses. Manual extraction of letters, attachments, and supporting documentation resulted in high error rates and inconsistent interpretation of unstructured data.

High dependency on human intervention limited scalability, increased turnaround times, and elevated operational costs. The client required a fully autonomous, high-accuracy solution that could reduce manual workload, improve compliance precision, accelerate dispute resolution, and significantly lower costs.

Sutherland Solution

End-to-End Autonomous Disputes Lifecycle Powered by the BFS Agentic AI Hub

Sutherland deployed a multi-agent, end-to-end Agentic AI system that autonomously executes every step of the disputes lifecycle.

Dispute Master Agent
The Master Agent monitors incoming dispute queues and identifies new cases in real time. It triggers downstream agents autonomously, coordinates decisions through an agentic control loop, ensures seamless case progression, and transitions smoothly to the next case upon completion.
Indexing AI Agent
This agent logs into the disputes system automatically and fetches cases using control numbers. It flags duplicates or incomplete records and hands off validated cases to the next agent in the workflow.
Dispute Categorization Agent
The categorization agent classifies disputes into General, Specific, or Identity Theft categories. It continuously learns from historical outcomes and regulatory patterns, delegates tasks to specialized sub-agents, and triggers parallel workflows for faster processing.
Extractor Agent
The extractor agent searches each control number across the centralized disputes platform and utilizes APIs to pull data from multiple source systems. It parses letters, attachments, and other unstructured content, extracting critical attributes such as account status, consumer details, dispute codes, and relevant metadata.
Reporting Agent
This agent connects with Power BI, SQL databases, and internal banking tools to gather account insights and historical transaction data. It produces enriched and structured summaries that support downstream validation and decision-making.
Validation Agent
The validation agent applies bank-specific business rules and regulatory requirements. It compares extracted data against source records, determines the appropriate response code and compliance condition code, updates all response fields autonomously in the disputes system, and submits the final dispute response before returning control to the Master Agent.

Together, this agentic ecosystem transformed disputes handling into a self-orchestrating, intelligent, and compliance-driven operation.

The Outcome

Higher Productivity, Scalable Peak Handling, and Improved Customer Experience

With the Agentic AI-driven disputes model in place, our client established a scalable and future-ready framework to manage dispute operations.

The solution delivers 50 to 60 % improvement in productivity and operational efficiency by eliminating repetitive manual tasks across retrieval, extraction, categorization, and validation stages. The autonomous model enables the client to manage high-volume peaks without additional staffing, ensuring operational resilience and cost optimization.

Customer complaints related to disputes have reduced by 60 to 70 % due to improved accuracy and faster turnaround times. Faster response cycles are expected to drive measurable uplift in CSAT, strengthening customer trust and brand reputation.

With the successful deployment of the BFS Agentic AI Hub for disputes, our client has established a digitally intelligent, compliance-first operating model capable of scaling with business growth while maintaining speed, accuracy, and customer-centricity.