What problem does it solve?
Card disputes are a volume problem wrapped in a rulebook. Each network defines its own reason codes, evidence requirements and deadlines, and revises them regularly. For every dispute an analyst at the issuer, the acquirer or the merchant has to find the transaction, pull authorisation and 3DS records, delivery or usage evidence and prior correspondence, decide whether to accept or fight, and write the case in the format the network expects, before the window closes.
Much of that work is low value but unforgiving. A missed deadline usually means the case is lost, weak packages lose winnable cases, and first party misuse (a customer disputing a purchase they made) is hard to separate from genuine fraud. Dispute volumes keep rising, so teams that work by hand grow with them. The customer facing side, where a cardholder first reports "I do not recognise this charge", is a separate job; this page is about what happens after the case is opened.
- Visa reports that it processed 106 million disputes globally in 2025, a 35% increase since 2019.Visa Unveils New Services to Modernize Dispute Resolution Process (2026)
How does it work?
- Ingest and classify. New disputes, retrieval requests and pre dispute alerts arrive from the network systems. The agent maps each one to the reason code and the applicable rule set.
- Gather the evidence. It pulls authorisation and clearing data, 3DS and device records, delivery and usage logs, refund history and correspondence, and checks them against the evidence the reason code requires.
- Recommend accept or fight. It estimates the chance of winning from the evidence and past outcomes, and recommends refunding, accepting or contesting, with reasons.
- Assemble and draft. For contested cases it builds the package in the network's format and drafts the rebuttal narrative from the evidence.
- Submit and track. An analyst approves the package where required, the system submits it, tracks each deadline through pre arbitration and arbitration, and records the outcome for learning.
- Audience
- Back office
- Autonomy
- Supervised agent
- Adoption
- Early adopters
- Channels
- Internal tools, API and system to system
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
| KPI | Median | Reported range | Data points | Claimed by |
|---|---|---|---|---|
| Hours saved | Not pooled | 20 hours | 1 | 1 vendor |
Value drivers: Lower cost to serve, Risk and loss reduction, Speed and cycle time, Employee productivity.
Indicative value
A card issuer or acquirer working 200,000 disputes a year
USD 300,000 to USD 1.6 million
Dispute handling effort avoided per year
How this is calculated
Formula: disputes * minutesPerDispute / 60 * effortReduction * costPerHour. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Disputes worked per year disputes, disputes per year | 200,000 | 200,000 | The reference organization. Replace with your own dispute volume. |
| Analyst minutes per dispute today minutesPerDispute, minutes per dispute | 15 | 30 | Editorial assumption covering evidence gathering, decision and package. Replace with your own time study. |
| Share of analyst time the AI removes effortReduction, fraction of time per dispute | 0.2 | 0.3 | Editorial assumption, capped at the one handle time figure on this page (a vendor reports nearly 30% lower handle time per assignment); analysts still approve contested cases and write offs. Replace with your own. |
| Fully loaded dispute analyst cost per hour costPerHour, USD per hour | 30 | 55 | Editorial assumption, replace with your own. |
What it leaves out: Labour only. It leaves out recovered losses from better packages and fewer missed deadlines, fees avoided through pre dispute refunds, and the cost of the platform and network integrations.
Who already uses it?
2 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Visa
United States · Payments and cards · 2026
In April 2026 Visa announced six new and enhanced dispute resolution tools. For issuers and acquirers they include Dispute Intelligence (predictive models that support case by case decisions, generally available), Dispute Doc Analyzer (AI summaries of merchant documents for issuer analysts and auto populated questionnaires for acquirers) and Visa Dispute Case Manager, which unifies dispute workflows from intake to resolution. Merchant tools cover pre dispute handling, generative AI representment responses and Compelling Evidence 3.0 to reduce friendly fraud. Several tools are still in pilot or planned for late 2026; no outcome figures were disclosed.
No outcome disclosed.
GitHub
United States · Technology and software · 2025
GitHub Sponsors, the platform through which people fund open source maintainers, uses Stripe Smart Disputes, which generates and submits evidence to contest chargebacks automatically. Before, the team reviewed disputes by hand and rarely contested them because gathering evidence took too long. The Stripe case study reports that the team now saves four to five hours of work a week and headlines an average reduction of 20 hours a month in time spent on disputes.
- Hours saved: 20 hours, per month, on average
"Time spent addressing disputes reduced by 20 hours per month, on average"
Claimed by: vendor
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Dispute history with reason codes, evidence submitted and outcomes
- Current network rules and evidence requirements per reason code
- Access to authorisation, clearing, 3DS, device and delivery data
Systems to integrate
- Card network dispute systems, such as Visa Resolve Online
- Card management and transaction processing systems
- Fraud and authentication platforms
- Merchant order, delivery and refund systems (acquirer and merchant side)
- Pre dispute alert services
Complexity: Medium
The networks already provide structured dispute systems and APIs. The work is gathering evidence from many internal systems and keeping the rule logic current with network releases.
- 1
Segment disputes by reason code and value
Find the reason codes with the most volume and the most avoidable losses. Low value disputes are often best refunded automatically; high value ones need the best packages.
- 2
Build the evidence map
For each reason code list the evidence that wins, where it lives and how to fetch it. This map is the core asset, with or without AI.
- 3
Automate assembly before decisions
Let the AI gather evidence and build packages while analysts decide, then measure package completeness and win rates.
- 4
Add recommendations with thresholds
Allow automatic accept or refund below a value threshold and on alerts, and keep analyst approval for contested and high value cases.
- 5
Keep the rules current
Assign an owner to network rule releases and run regression tests on the rule logic before each release date.
Guardrails
- Rebuttals use only evidence from the case; the model never invents facts or documents
- Rules and deadlines come from a maintained rule set, not from the model's memory
- Write offs and contested cases above the threshold need analyst approval
- Cardholder data masked in prompts and logs in line with PCI DSS
KPIs to instrument
- Win rate on contested cases, by reason code
- Deadlines missed
- Analyst minutes per dispute
- Share of disputes resolved by pre dispute alerts or automatic refund
- Net losses from disputes as a share of sales or volume
Human in the loop
Analysts approve contested cases, high value decisions and write offs, and handle suspected fraud or hardship. A quality team samples automatic accepts and refunds each week, and the rule owner signs off changes when networks publish new rules.
Common failure modes
- Outdated rules
- A network rule change invalidates the evidence logic and cases start losing. Version the rule set and test it on every release.
- Fighting everything
- Automation makes it cheap to contest, so weak cases are fought and fees rise. Use win probability and value thresholds.
- Invented evidence
- A drafted rebuttal describes evidence that is not in the package. Link every statement to an attached document.
What are the risks and rules?
EU AI Act
Minimal risk
Dispute processing between issuers, acquirers and merchants is not listed in Annex III. It is not an evaluation of creditworthiness or credit scoring under Annex III point 5(b), and because cardholders do not interact with the system directly, the Article 50(1) transparency duty for AI that talks to people does not apply. Article 50(2) marking of generated text is a duty of the provider of the AI system that generates it, which includes an institution that builds its own dispute drafting agent and puts it into service under its own name. A drafted rebuttal built from attached case evidence performs an assistive function for standard editing of that evidence and does not substantially alter the underlying input, so it falls under the Article 50(2) exception and does not need machine readable marking. With that point checked, the tier stays minimal. A customer facing intake agent is assessed separately.
Rules that apply
Guidance
- Visa Core Rules and Visa Product and Service Rules (Visa, Global). The public rulebook that defines dispute conditions, evidence and time limits for Visa transactions.
Controls to put in place
- Versioned rule set per network with an owner and release testing
- Deadline tracking with alerts well before each network cut off
- Audit trail of evidence, decision, approver and outcome for every dispute
- Documented value thresholds for automatic accept and refund
Frequently asked questions
- How are chargeback operations different from dispute intake?
- Intake is the customer facing moment when a cardholder reports a charge and the case is opened. Chargeback and representment is the back office work that follows: reason codes, evidence, network packages, deadlines and arbitration, on the issuer, acquirer and merchant side.
- What results have been published?
- Mostly vendor figures. Stripe reports that GitHub Sponsors, using its Smart Disputes product to generate and submit dispute evidence, spends 20 hours a month less on disputes on average, and the dispute platform vendor Quavo reports that institutions using its product cut average handle time per assignment by nearly 30%. Visa announced AI dispute tools for issuers, acquirers and merchants in April 2026, some generally available and some in pilot, without outcome figures.
- Should the AI decide to write off a dispute?
- Below an agreed value threshold, automatic accept or refund can make sense, because fighting a small dispute can cost more than it recovers. Above it, and for contested cases, an analyst should approve, because the network rules and the economics of each case differ.
How to cite this page
Blits.ai AI Use Case Library, "AI for chargeback and representment operations", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/chargeback-and-representment. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published