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 to 1100 hours | 6 | 6 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?
10 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.
Smallpdf
Switzerland · Technology and software · 2026
Smallpdf, a Zurich based document management platform used by tens of millions of people a month, ran its subscription business across three payment providers (Braintree, Stripe and Adyen) with separate dispute workflows and evidence requirements, while billing context lived in Recurly. It partnered with Chargeflow, using a prebuilt Recurly integration, to unify evidence gathering, case enrichment, response drafting and submission across all three processors from one place. Chargeflow's own case study reports Smallpdf recovered 55,000 US dollars in disputed revenue and generated an estimated 45,000 US dollars in operational savings in its first six months, with automation saving more than 1,100 hours of manual dispute work over that period.
- Revenue recovered: USD 55,000, in the first six months
"In its first six months with Chargeflow, Smallpdf recovered $55K in disputed revenue and generated an estimated $45K in operational savings."
Claimed by: vendor - Cost savings: about USD 45,000, in the first six months
"In its first six months with Chargeflow, Smallpdf recovered $55K in disputed revenue and generated an estimated $45K in operational savings."
Claimed by: vendor - Hours saved: at least 1100 hours, in the first six months
"Automation saved more than 1,100 hours of manual dispute work, while improving revenue recovery and win rates."
Claimed by: vendor
The Beard Club
United States · Retail and ecommerce · 2026
The Beard Club, a direct to consumer ecommerce brand for beard growth and grooming products, was spending about 40 hours a week manually responding to disputes and still felt it was guessing at what card issuers wanted. It adopted Chargeflow's automated platform to gather evidence and submit dispute responses. Chargeflow's own case study reports that the 40 hours of weekly manual work fell to about one hour of high level review, alongside improved win rates and revenue recovery.
- Hours saved: about 40 hours, per week
"What previously required 40 hours per week now requires about one hour of high-level review."
Claimed by: vendor
Yves Rocher
France · Retail and ecommerce · 2026
Yves Rocher, a global cosmetics and skincare brand with retail and ecommerce operations across Europe and North America, used to manage chargebacks by hand across several regions and payment processors, with disconnected tools for evidence, deadlines and submissions. It adopted Chargeflow's automation product, with Chargeflow Alerts added where available to prevent disputes before they escalate, and now runs the whole chargeback lifecycle from one dashboard. Chargeflow's own case study reports a 50 percent increase in the chargeback win rate and more than 280 hours saved.
- Recovery uplift: 50%, not stated
"Yves Rocher Increases Chargeback Win Rate by 50% and Saves 280+ Hours with Chargeflow"
Claimed by: vendor - Hours saved: at least 280 hours, not stated
"Yves Rocher Increases Chargeback Win Rate by 50% and Saves 280+ Hours with Chargeflow"
Claimed by: vendor
Fanatics
United States · Retail and ecommerce · 2025
Fanatics Live, the sports collectibles marketplace run by Fanatics, had its Trust and Safety director managing every chargeback by hand, spending more than 25 hours a week on the work. Fanatics adopted Chargeflow to automate dispute processing, add real time fraud alerts and give the team dashboards on fraud trends, moving from a reactive process to an automated one. Chargeflow's case study, quoting Fanatics' global director of trust and safety, reports over 800,000 US dollars in revenue recovered in the first year, alongside a stated 25 or more hours saved each week.
- Revenue recovered: at least USD 800,000, in the first year
"Since implementing Chargeflow, it's saved us over $800,000 in revenue this year alone."
Claimed by: organization - Hours saved: at least 25 hours, per week
"25+ hours saved weekly"
Claimed by: vendor
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
HexClad
United States · Retail and ecommerce · 2025
HexClad, a cookware brand, used to prepare each chargeback dispute by hand ahead of high volume periods such as Black Friday. It adopted Chargeflow's automated platform, which handles dispute notifications, evidence gathering and response submission end to end and gives the team real time dashboards for the cases still needing a decision. Chargeflow's own case study, quoting HexClad's head of customer service, reports a 59 percent increase in chargeback recovery rates and 199 hours saved in manual dispute management work.
- Recovery uplift: 59%, not stated
"HexClad achieved a 59% increase in chargeback recovery rates, providing a significant boost to their revenue protection efforts."
Claimed by: vendor - Hours saved: 199 hours, not stated
"Chargeflow's automated platform saved HexClad 199 hours, allowing the team to redirect resources toward other strategic initiatives."
Claimed by: vendor
N26
Germany · Banking · 2025
N26, a fully licensed digital bank operating across 24 European markets, deployed Claude on Amazon Bedrock across more than 15 internal customer operations applications. For chargeback requests, Claude translates documentation, analyzes the claim and recommends a decision on complex cases with several verification requirements, with a product manager saying it caught details a human reviewer missed. The chargeback process is one of several Claude covers, alongside physical claim letter classification, financial crime analysis and a customer facing virtual assistant.
No outcome disclosed.
Obvi
United States · Retail and ecommerce · 2025
Obvi, a direct to consumer health and wellness supplement brand, had been paying a freelancer per chargeback case to fight disputes by hand and was winning only about one in five. It adopted Chargeflow's automated platform, which gathers evidence and submits the dispute response. Over almost six months, Chargeflow's own case study reports Obvi's win rate rose from around 20% to 54%, a 170% increase, while the cost of handling disputes fell because Chargeflow's fee is a share of cases recovered instead of a flat freelancer rate.
- Recovery uplift: 170%, over almost six months
"After integrating Chargeflow into their operations for almost six months, their win rate soared to 54% - a remarkable 170% increase."
Claimed by: vendor
Wordtune (AI21 Labs)
Middle East · Technology and software · 2025
Wordtune, the AI writing tool made by AI21 Labs, managed chargebacks in house as its user base grew quickly, with a win rate its own head of user operations called "less than 10%" and hiring an outsourced specialist proving too costly. It adopted Chargeflow's automated chargeback platform, integrated in two clicks with no infrastructure changes, to gather evidence, draft responses and submit disputes on time. Chargeflow's own case study reports Wordtune's win rate increased 4.3 times over, with on time submissions reaching 100 percent even as chargeback volume tripled.
- Recovery uplift: 4.3x, not stated
"Wordtune’s chargeback win rates increased by 4.3x and their submission rate has improved to 100% all while seeing a 3x increase in chargeback volumes as they grew."
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
- 29 September 2026: Added deployments: Obvi, The Beard Club.
- 29 September 2026: Added deployments: HexClad, Fanatics.
- 27 September 2026: First published