For most organizations, the dispute journey begins and ends with intake. A customer spots a charge they do not recognize, files a claim, and expects the problem to be solved. Digital channels have made that first step faster and easier than ever.
But while the front end of chargeback management has modernized, downstream is a very different scenario. Behind every claim lies a far more complicated question—who’s going to pick up the bill?
The answer is rarely clear. Liability shifts between bank, merchant, network, and customer through a maze of provisional credit, evidence gathering, representment, and tightly regulated deadlines.
What appears straightforward from the outside is, internally, a complex negotiation across systems, teams, and rules. As dispute volumes continue to rise, this hidden second half of the process is where cost, delay, and risk concentrate. Orchestrating it is central to building real fraud and dispute resilience.
This transformation is what we’re looking at in this article, a follow-up to our whitepaper Rewriting the Rules: How AI Is Transforming Fraud and Dispute Resolutions.
The Liability Maze Behind Every Dispute
Global dispute volumes are rising fast. There were an estimated two hundred thirty‑eight million global chargebacks in 2023, a figure predicted to climb 42 percent to three hundred thirty‑seven million this year.[1]
A Mastercard-backed study projects that sellers will lose $15 billion to fraudulent chargebacks in 2025 alone. Total chargeback volume was expected to hit $33.8 billion in 2025, climbing to $41.7 billion by 2028.
Each one triggers investigation, coordination, and financial exposure – regardless of whether fraud actually occurred – and the settlement is where the real work begins. Once a dispute is filed, the process shifts into a multi-party decision framework.
The bank issues provisional credit, returning funds to the customer immediately while the investigation continues. The merchant may contest the claim through representment. The payment network enforces its rules. Evidence is gathered, reviewed, and assessed. Regulatory timelines start counting down, and at each stage, liability can switch.
What feels simple to the customer is, in reality, a structured financial negotiation involving multiple actors and strict compliance requirements. Sometimes the merchant absorbs the loss. Sometimes the bank does. Sometimes the customer ultimately repays the amount when provisional credit is reversed.
There is clearly operational tension here. Institutions must act quickly to protect customers, yet need accurate data to avoid absorbing unnecessary losses. Meanwhile, evidence must be collected, verified, and matched against complex scheme rules, all against a ticking clock.
The Operational Weight of a Fragmented Back-End Workflow
Consider a typical case. A customer disputes a $280 online purchase, claiming they never received the item. The bank issues provisional credit immediately, restoring the customer’s balance.
Behind the scenes, the merchant receives a chargeback notice and begins representment. Delivery confirmation, timestamped photo evidence, and transaction logs are submitted.
Weeks later, the evidence is reviewed. The item was delivered correctly, so the dispute is ruled invalid. The provisional credit is reversed, and the customer is rebilled.
From the outside, this looks straightforward. Internally, though, it triggers the following processes:
- Customer service intake.
- Fraud team review.
- Evidence submission and verification.
- Scheme-level processing.
- Financial adjustment and communication.
- Compliance tracking.
Multiply this by millions of disputes, and the operational weight becomes clear.
This fragmentation drives structural inefficiencies. Time is also a factor: resolution cycles can stretch thirty to ninety days or longer. Evidence arrives in inconsistent formats, and decision-making varies by case. Customers receive limited transparency, all the while operational costs accumulate. And because many workflows remain manual, scalability is a constant challenge.
Across the industry, institutions spend heavily on chargeback management. For every $100 in fraudulent chargebacks, merchants may spend around $35 just managing the dispute process itself. The cost is not just the loss—it is the work required to resolve it.
The Hidden Cost of Getting Liability Wrong
Even though organizations invest heavily in fraud prevention, fraud losses continue rising because operational complexity and fragmentation remain. Every dispute becomes a balancing act between speed, fairness, and compliance.
Liability decisions bring financial and reputational risks:
- Refund too easily = you risk encouraging abuse.
- Reject too aggressively = lose the trust of your customers.
- Miss regulatory deadlines = your organisation may be penalised.
- Lose representment = see your revenue disappear.
- Reverse provisional credit = increase friction for customers.
And as volumes grow, manual decisioning becomes increasingly unsustainable.
How Automation Improves Chargeback Management
While dispute settlement is inherently complex, much of it is rule-based and repeatable. Eligibility windows, thresholds, documentation completeness, and scheme requirements follow predictable logic. None of these decisions need to sit in human queues.
Automation can instantly apply policy rules, identify missing information, and route cases correctly. Document intelligence can extract key data from invoices, receipts, and delivery confirmation. Behavioural analysis can flag repeat offenders or suspicious patterns. Workflow orchestration can move cases forward without waiting for manual coordination.
The twin outcomes should be speed and consistency. Cleaner audit trails with lower operational costs, as well as faster resolutions and fairer outcomes for customers and merchants.
From Intake Automation to End-to-End Orchestration
Digital channels, guided workflows, and smarter data capture have made it faster and easier for customers to file claims. But real transformation happens deeper in the process, connecting and conducting the full lifecycle, from claim to final settlement.
That means previously isolated signals like customer behavior, transaction history, and dispute patterns must all be viewed together to build a clearer picture of intent and risk.
Rather than slow, manual interpretation, rule-based liability decisions can be automated wherever possible, ensuring consistent, real-time application of policy. Simultaneously, evidence and representment workflows need to be streamlined so documentation can be collected, analyzed, and routed without delay.
Equally important is transparency. Customers expect visibility into what is happening and why, especially when their money is involved. And throughout the process, institutions must meet strict regulatory requirements, without slowing resolution or introducing unnecessary friction.
When these elements operate together, chargeback management becomes coordinated and consistent. Instead of a costly operational burden, there is a structured, resilient process. The future of chargeback management isn’t just faster intake. It’s end-to-end orchestration that connects systems, decisions, and intelligence across the entire lifecycle.
To explore that future, and discover how AI and automation are transforming dispute and chargeback operations, download our ‘Rewriting the Rules’ whitepaper now.
