The $41.7B disputes problem AI can solve
Banks lose billions every year to disputes that take too long, cost too much, and deliver inconsistent outcomes. Manual processes can't keep pace—and the gap is widening.
$41.7B
Projected global cost of chargebacks by 2028 (~$20B in North America).
324M
Projected global chargeback volume by 2028 — up ~24% from ~261M in 2025.
$9–$10+
Issuer’s cost to process a single dispute, before write-offs or recovery losses.
$15B
Projected U.S. losses from APP scams by 2028, over instant, irrevocable rails.
The challenges
- Manual and error prone: Dispute process is still heavily manual - driving errors, elevated operating costs, & adverse financial impact
- Rising costs: More disputes mean more headcount—an unsustainable equation.
- Global impact: Global costs are projected to hit $41.7 billion by 2028, with ~$20 billion in North America.
- Inconsistent classification & decisioning: Decision quality varies by analyst, shift, and workload—creating loss leakage and compliance risk.
- Evidence overload: Analysts manually review large, unstructured merchant evidence documents, and the outcome directly impacts liability, win rates, and recoveries.
- Growing fraud: First-party fraud exceeds 45% of chargebacks; APP scam losses are projected to reach $15 billion in the U.S. by 2028
- Tightening regulations: Reg E, Reg Z, and Visa's VAMP are imposing increasingly stringent timelines and narrowing the margin for error.
62% of consumers said how their bank handled a dispute influenced their trust more than the fraud event itself.
Poor dispute handling doesn't just cost money. It costs customers!
The solution: Automate the decision, not just the workflow
Disputes are a decisioning problem. AI solves it by automating the decision itself—not just the workflow around it—with humans handling only the exceptions that truly require judgment.
- Autonomous decisioning. Generative and agentic AI resolve routine disputes end-to-end—no human required for every case.
- Targeted agents. Specialized agents eliminate bottlenecks at intake, classification, summarization, and evidence analysis.
- Orchestrated workflows. Agents operate as one coordinated system, not a set of disconnected tools.
- Human-in-the-loop controls. High-confidence cases resolve automatically; complex ones route to a specialist, fully pre-assembled.
- Full auditability. Every decision is logged with a traceable rationale—ready for regulators and networks.
Inside the whitepaper
- A clear implementation roadmap: Two phases, from targeted agents on high-volume tasks to fully orchestrated, end-to-end decisioning.
- A real-world case study: Know how EXL helped a leading bank achieve 5–7x ROI in pilot conditions with an AI evidence-analysis agent.
- A reference architecture: A five-layer design that keeps AI modular, governed, and reusable across all dispute types.
- Regulatory guidance: How to build governed autonomy that satisfies Reg E, Reg Z, and network audit requirements.
The question isn't whether to automate dispute decisioning—it's whether you do it before or after your competitors do!