At Justt’s ChargebackX 2025 conference, Worldpay’s Director of Product Kimberly Singleton spoke about strategies that can curb chargeback volumes and help merchants to meet increasingly strict deadlines. This article outlines the specific pain points merchants now face, and Singleton’s expert advice for getting back on track.

Mounting Pressure: Visa and Mastercard Fees

In addition to the notable Visa Acquirer Monitoring Program (VAMP), in April this year Visa announced a host of additional rule changes for Visa merchants and acquirers. One of the costliest rules introduced was a $7 fee for chargebacks that expire without merchant response – more than doubling the previous $3 penalty.

Perhaps the greater challenge is the fees placed on responding after the first 10 days. To avoid those fees, evidence gathering, transaction research, and representment assembly must now be completed in a third of the time they were formerly allocated.

Mastercard takes a different approach, penalizing ineffective representments rather than delayed responses. Their $15 pre-arbitration fee applies when merchants represent a case but then accept defeat if the case goes to the pre-arbitration phase. The message is that if you’re going to fight, you need to be sure you have the right evidence to win.

The Mastercard network has also streamlined certain dispute reason codes in issuers’ favor. For authorization and EMV-related reason codes, issuers can skip straight to arbitration if they deem a merchant’s response insufficient, and with arbitration fees exceeding $500, the stakes for getting responses right have never been higher.

It’s important for merchants to note that these fees originate from the card schemes and are passed on to acquirers. As such, merchants should check whether their acquirer is passing these fees down to them, and whether there’s room to negotiate the terms.

But negotiating fees is just one lever. What can merchants do to protect themselves against these penalties?

Use the Full Spectrum of Pre-Dispute Tools, But With Some Caveats

Pre-dispute solutions promise to stop chargebacks before they happen, but success depends on matching the right tools to your business model, and using them strategically.

Ethoca Alerts and Verifi CDRN are common pre-dispute solutions that provide advance warning of incoming chargebacks in exchange for issuing refunds between 48-72 hours from receiving the alert. As Singleton remarks, the value of these tools may extend well beyond chargeback avoidance: “If you’re delivering physical goods, you may be able to prevent order fulfilment by getting these alerts earlier, ensuring that you don’t lose a product as well as the transaction value”.

However, these solutions require dedicated resources. “These are not like a set-and-forget product,” Singleton warns. “You have to review the alert and take action. That’s a strain on your capacity.” Merchants should also avoid reflexive refunding. Singleton advises that merchants “Don’t just blindly refund every dispute alert. Review them. The risk of chargeback is sometimes worth it.”

Data is Your Saving Grace. Collect It and Use It

Comprehensive data collection forms the foundation of effective chargeback management; many disputes can only be solved with extensive evidence. “We know fraud and authorization is all about the data,” Singleton notes. “Make sure you have easy access to that data to support your chargeback responses.”

While most merchants are aware that responses are highly data-reliant, fewer take the importance of pre-dispute tools into account. Singleton emphasizes that “inquiry products are driven on data. They are effectively worthless unless you have the data that makes them valuable.”

Multi-acquirer merchants need to think carefully when navigating this challenge. “Some merchants will get an ARN (acquirer reference number) from one acquirer but not from the other. This means that they will struggle to find associated transaction data.” The implication is clear – smart multi-acquirer merchants need to search for ways to consolidate their data across the board, or face the penalties.

Choose Carefully When Considering Outsourcing or Chargeback Management Tools

Outsourcing decisions involve more nuanced considerations than simple cost comparisons – especially given Visa’s new fee structures. Singleton explains that “Many merchants simply don’t have the capacity to effectively and efficiently manage chargebacks. They might throw lots of responses out, but they don’t stick, resulting in high loss rates.”

Of course, the predominance of manual, template-based systems means that many outsourced solutions face the same problems with volumes and quality fluctuations. This often leads to the cost of labor being passed on to the merchant, who is left with little to show for it.

Singleton identifies success-based fee models as an excellent way to ensure that your external chargeback solution is actually delivering on revenue retention. However, she insists that merchants must choose carefully. “Look at your incoming disputes,” Singleton advises. “What are you winning today versus what the provider is promising? Maybe they’re promising a 50% win rate, but they’re going to keep 25% of what they save you. Which one’s higher?”

Automate Acceptance and Response for Best Results

Visa’s increased expiry fees and increased fees for late responses arguably make some form of automated acceptance essential for maintaining profitability – but only for cases that deserve it. “Consider all aspects of the dispute,” Singleton recommends. “Consider the dollar amount, consider the merchant, and consider the reason code.”

Small-value disputes often make the most sense for automatic acceptance. “Is my employee’s time working this chargeback really going to make a difference with a $5 dispute?” Singleton asks. “If that’s a true fraud chargeback for $5, it’s not worth fighting.” The key is avoiding blanket policies: “I don’t recommend a set-and-forget rule, but maybe it’s automation for every $5 chargeback or under for 10.4 or 4837 reason codes.”

(Of course, with Justt – which is a fully automated system where the marginal cost of handling another dispute is zero – your calculus might be different, and you would lean more towards disputing every winnable case and only accepting chargebacks where you are more likely to lose a dispute.)

Where dispute volumes are high, Singleton advocates for smart solutions: “Look at things like AI-driven winability scoring to focus on efforts where they will be most effective. This means you win what you know you can win, and forfeit lost causes, which will vastly reduce your risk of chargebacks fees.”

For disputes worth fighting, automation ensures deadline compliance while maintaining quality. Modern solutions such as Justt will automatically gather evidence, create customized representments, and submit responses – all while conducting A/B testing to optimize future performance. “These things learn over time,” Singleton explains. “They will adjust your response to help boost your win rate. They see what works, what doesn’t, adjust accordingly.”

This is particularly useful for dealing with varying issuer demands. As Singleton explains, “Different issuers prefer data in different orders. Some want the data quick and concise, and some want pages of documentation. AI and machine learning-based solutions really come into play here, because they can learn individual issuers’ preferences and respond in kind.”

Staying Ahead of the Curve

If decades-long trends persist, response windows will only tighten further while fees keep rising. While Singleton advises that smart chargeback management, automated acceptance strategies, and data collection are key parts of the solution, she advocates awareness as a first line of defense: “Keep up to date with chargeback rules, and ensure you understand them fully. They update twice a year in April and October. They’re ever changing.”

The post 4 Ways to Prepare for Rising Card Scheme Fees and Tightening Deadlines appeared first on Justt.

But these tools live in different layers of the chargeback lifecycle, meaning that each has a different function and serves different needs. Since it’s easy to get confused – we’ve decided to map the different tools so that merchants can easily find the ones they need.

This post breaks down the ecosystem, clarifies what each category actually offers as a benefit, and shows how these layers work together to build a complete chargeback management strategy.

Summary

• True chargeback automation refers to the recovery layer, where technology directly fights and wins disputes.
• Chargeback prevention and alert tools remain essential and work best as part of an integrated stack of chargeback management.
• Each layer of chargeback management tools automates a different part of the lifecycle: risk, resolution, or recovery.
• The most effective approach connects these layers through data, creating a system that learns and improves over time.

The challenge with the term “chargeback automation”

Search Google or ChatGPT for chargeback automation tools and you’ll see names like Ethoca, Verifi, Sift, Riskified, Midigator and Justt, all positioned around automation.

They’re all legitimate solutions. But they don’t automate the same thing.

Some spot fraud before it becomes a chargeback. Others intercept disputes and send alerts before they hit your chargeback ratio. And some, like Justt, automate the recovery process after a chargeback occurs.

Individually, each category has value. Taken together, they form the foundation of effective chargeback management. But it helps to understand what “automation” actually means at each layer,…and what it doesn’t.

The three layers of chargeback management

The modern chargeback ecosystem breaks down into three main layers:

1. Fraud prevention and chargeback insurance tools – stop fraudulent transactions before they lead to chargebacks.
2. Pre-dispute / alert tools /chargeback prevention tools – detect and resolve potential chargebacks before they become formal disputes.
3. Chargeback automation platforms – handle chargebacks that occur and automate the process of fighting and winning them, leading to revenue recovery.

Each layer uses automation differently. And they work best when they’re connected.

Layer 1: Fraud prevention tools – Automating fraud control

Purpose: Prevent chargebacks before they happen

Examples: Sift, Riskified, Forter, Signifyd

How they automate:

• Real-time fraud scoring and decisioning
• Adaptive rules that adjust risk thresholds by product, geography, or buyer profile
• Continuous learning from transaction outcomes

What they cover: Fraud-based disputes and payment risk

What they don’t cover: Friendly fraud, service disputes, or legitimate customer dissatisfaction, all of which now make up a large share of chargebacks

Fraud prevention tools are essential. They protect the top of the funnel, reduce risk exposure, and help maintain healthy chargeback ratios. But they don’t address disputes that happen after an authorized transaction, including friendly fraud, which is where many merchants see the bulk of their chargeback volume today.

2. Pre-dispute and alert tools – Automating early resolution

Purpose: Identify and resolve potential chargebacks before they reach the card network

Examples: Verifi and Ethoca

How they automate:

• Real-time notifications when a cardholder queries a charge.
• Automated refund workflows based on merchant-defined rules.
• Integration with acquirers and PSPs to intercept disputes in progress.

What they cover: Early-stage disputes that can be defused with proactive communication or refunds

What they don’t: Chargebacks that have already been filed, or the analysis and evidence-building needed to fight them

Pre-dispute tools are crucial for keeping chargeback ratios under control and maintaining compliance with monitoring programs. They give merchants an early warning system and the ability to resolve disputes before they escalate. Not all merchants need them. Typically merchants that do need chargeback prevention tools have high chargeback ratios or seasonal business and they use alerts to ensure they don’t exceed the thresholds.

The key is knowing when to use them, and when not to. Auto-refunding every dispute keeps your ratio low, but it also surrenders revenue you might have won back. That’s where recovery automation comes in.

Layer 3: Changeback recovery automation – Automating dispute resolution

Purpose: Automate the representment process once a chargeback has occurred, recovering revenue that would otherwise be lost

Examples: Justt

How they automate:

• AI-powered data collection from multiple sources (PSPs, CRMs, processors)
• Dynamic evidence package creation tailored to reason code, issuer, and card network
• Automatic submission and tracking of representments
• Continuous learning to improve win rates and efficiency over time

What they cover: The full dispute lifecycle, from case identification to optimization.

This is where comprehensive chargeback automation lives: technology replaces manual case handling with intelligent, data-driven workflows.

Automation here saves time but also improves accuracy, consistency, and ROI.

Platforms like Justt enable merchants to scale dispute management efficiently and improve win rates, through:

• Pulling complete evidence from every relevant system
• Tailoring submissions to what specific issuers and networks actually want to see
• Meeting every deadline
• Learning from outcomes and refining strategy over time

Platforms like Justt enable merchants to scale dispute management efficiently while integrating with upstream systems to create a connected ecosystem.

Why it takes all three layers

Each tool type serves a specific purpose:

The real power comes when these layers are connected. When your recovery platform feeds dispute outcome data back into your prevention models or alert rules, you create a closed feedback loop across the entire chargeback lifecycle.

For example:

• Recovery data shows you win 80% of “item not received” disputes from a specific product line → your alert system stops auto-refunding those
• Dispute patterns reveal issues with a particular shipping carrier → that insight feeds back into your fraud prevention logic
• Reason code analysis identifies trends → informing both prevention strategies and early resolution workflows

Leading merchants orchestrate these tools so each layer makes the others smarter.

So, what is “chargeback automation,” really?

“Chargeback automation” gets used broadly to describe any technology that simplifies chargeback management. But in practice, it has a more specific meaning:

Chargeback automation is the use of AI and data-driven systems to automatically build, submit, and optimize dispute representments—not just issue refunds or block transactions.

That’s an important distinction. It doesn’t diminish the value of prevention or alerts. Instead, it clarifies where automation reaches its fullest form. Prevention protects future revenue. Alerts manage risk and ratios. Recovery automation returns revenue from disputes that have already occurred.

All three matter. But only one gets your money back.

How Justt fits in the ecosystem

Justt is built to complement the other layers of a merchant’s chargeback stack.

It connects downstream to recover what prevention and alerts can’t address, while feeding performance data back upstream to inform risk decisions and early-resolution logic.

In practice, it works like this:

1. Your fraud prevention tool blocks suspicious orders → reducing preventable chargebacks
2. Your alert system intercepts pre-disputes → minimizing unnecessary chargebacks
3. Justt automates the rest → recovering revenue from legitimate disputes efficiently and intelligently

That’s a complete chargeback management ecosystem—one where each layer supports the next.

What makes Justt’s approach effective:

• Full automation — Complete evidence collection, package creation, and submission without manual work
• Adaptive intelligence — AI trained on millions of disputes that tailors strategy to card network, issuer, and reason code
• Closed-loop learning — Every dispute outcome feeds back into the system, making your entire stack smarter over time

For more detail on how Justt’s automation works, read here.

The future of chargeback automation

New technologies, from agentic commerce to advanced payment orchestration, are changing how and when chargebacks occur. Networks are expanding monitoring programs like Visa’s VAMP. Issuers are introducing more automated dispute handling.

In that environment, automation becomes even more critical, not just for scale, but for continued adaptability and intelligence.

Merchants will need tools that can handle growing dispute volumes automatically, integrate across payment systems for unified visibility, and use outcome data to continuously improve prevention and resolution strategies.

Ready to build a smarter chargeback stack?

Book a demo to see how end-to-end automation can recover more with less effort.

The post The chargeback tool ecosystem: explained appeared first on Justt.

At Justt’s ChargebackX 2025 conference, Ludovic Houri, Managing Director and Head of Merchant Services EMEA/APAC at JP Morgan, and Katie Steel, VP Security Solutions at Mastercard, shared insights on current trends, best practices, and the future of chargeback management. Hosted by Justt’s Co-Founder and Chief Risk Officer Roenen Ben-Ami, their discussion revealed that while rising dispute volumes entail real risk, merchants who adopt data-driven approaches can still reap well-earned rewards.

Travel, Subscriptions, and Strategic Errors: Chargebacks Go Sky-High

Chargeback growth is happening in almost every industry, but the rate of growth varies sector-to-sector. Travel represents one of the most dramatic increases, with disputes rising 800% since the pandemic. Houri stated that this is partly due to the complexity introduced by third-party booking platforms, but attributes the primary cause to communication failure: “It’s often easier to get a travel chargeback than a refund from a travel company.”

Subscription services face similar challenges, with chargebacks increasing 59% year-over-year. Likewise, much of this growth stems from strategic errors on the merchants’ part. “This is largely due to legitimating behaviour by merchants who refund or accept chargebacks too willingly because of the small transaction amount,” Steel noted. “This often proves to be a false economy, as it leads to more chargebacks from emboldened cardholders down the line.”

Both speakers identified friendly fraud, or first-party fraud, as one of the industry’s greatest challenges. Unlike true fraud, first-party fraud involves legitimate cardholders disputing valid transactions – whether intentionally or due to confusion. 75% of all disputes result from first-party fraud, and this number is rising fast. Steel warned that merchants need to take responsibility for these figures: “By representing without strong evidence, merchants are both losing that revenue and training cardholders to abuse that system.”

Prevention Strategies: Layering Your Chargeback Defense

While both speakers acknowledged the issue of rising chargebacks, neither viewed the challenge as insurmountable. Steel outlined a three-tier approach to chargeback prevention that addresses disputes before they occur. The first layer focuses on fraud prevention tools. “Practice AVS and CVV checks, use 3D Secure, monitor for red flags like address mismatches – but also arm yourself with AI detection tools,” Steel advised. The second layer addresses transaction clarity, using clear billing descriptors and network tools like Ethoca’s Consumer Clarity, while the third layer optimizes support for cancellations and refunds. 

Steel cited research showing that “84% of consumers find chargebacks easier than merchant refund processes.” By streamlining refund procedures, merchants can resolve issues directly rather than through the dispute system. Once disputes are initiated, tools like Ethoca Alerts allow merchants to issue refunds before chargebacks become formalized, avoiding lengthy and expensive battles.

Houri concurred with Steel’s three-tier system, but emphasised that merchants must operate swiftly to make it work. “The key element is speed”, he observed. “When something goes wrong in a transaction, reaction time is crucial. You want to prevent a problem becoming a chargeback. Use every predispute tool available before the dispute gets real.”

What Sets Top Performers’ Win-rates Apart?

Both speakers noticed that merchants who successfully manage chargebacks share certain characteristics. While some of these relate to avoiding chargebacks in the first place, merchants with high volumes and high win rates also showed significant commonalities. Some of these included: 

Automation and Data

According to Steel, “Top performers who experience high chargeback volumes achieve success through intelligent pushback. They’re using automated representment tools, providing robust documentation, and employing data-driven strategies. This means treating different claims differently, and using data to decide when to fight.” 

Houri concurred, remarking that “Many automated solutions allow merchants to use data beyond the classic representment elements, increasing the likelihood of a win”. He pointed to advanced data points like device IDs and IP addresses as powerful evidence. “When a cardholder claims ‘I was not there, it was not me,’ you can respond: ‘Yes, you were. It was your phone.’”

Education and Adaptability

Houri emphasized education as a major differentiator. “The rules are so important and they’re ever-evolving,” he noted. “Different staff have to deal with that, and not all of them have the same level of knowledge.” To remedy this, he recommends that merchants educate staff continuously and consult with acquirers about available chargeback handling education.

Steel notes that top performers also treat chargeback management as an ongoing process – “fluid, rather than a one-time fix”. They follow the data continuously and use systems that can adapt to changing behaviour in real time.

Picking Your Battles

A common merchant mistake involves fighting every chargeback indiscriminately, or forfeiting every dispute you receive. But while these approaches may save time, they often play havoc with revenue. Steel argued for strategic dispute management where each case is evaluated based on recovery potential, cost, and long-term impact:

“Merchants should establish rules based on minimum thresholds, transaction types, and reason codes. By fighting every chargeback you’re going to see higher costs, lower win rates, and risk decreasing approval rates with certain issuers.”

Conversely, Houri added that indiscriminate fighting creates problems with acquirers, remarking that “It’s not only the money and the cost; it’s the post-dispute impact it could have on the merchant-acquirer relationship, and any associated fees.”

What the Future Holds: Agentic Commerce and Chargebacks

The speakers offered contrasting views on how agentic commerce – where AI agents complete purchases for cardholders on third-party sites – will affect chargeback volumes. Their debate was characteristic of the conference, where Amazon’s “Buy for Me” agentic program and Google’s AP2 protocol attracted both praise and concern. All parties could agree on only one thing: agentic commerce is coming fast, and it’s going to change online business forever. 

Houri expressed cautious optimism, suggesting that agents operating with full knowledge of rules and systems could optimize transactions and assemble dispute elements systematically. “My gut feeling is that it could improve dispute results in terms of speed, cost and efficiency,” he said. “Simply put, replacing the human consumer with an agent consumer should lead to fewer judgments.”

Steel took a more skeptical stance, identifying several risk factors. “We pessimists in the industry feel that it may actually drive additional claims because of misinterpreted intent,” she explained. Language nuances – like a customer saying “violet shirt” when they meant “purple shirt” – could trigger disputes.

Overpersonalization presents another challenge. As AI systems make purchases based on inferred preferences, customers may dispute transactions with claims that agents were not carrying out their orders. Steel highlighted the concept of “next best” purchases, where agents select alternatives when preferred items are unavailable. “This can lead to mismatches in sizes, features or packaging, especially in categories like apparel and household goods.”

Preparing for Agentic Commerce

The urgency of preparation for agentic commerce is underscored by growing AI adoption rates and the increasing appeal of this technology for consumers. Weekly OpenAI users reached 700 million in September, up from 500 million at the end of March. Meanwhile, over half of consumers have replaced traditional search engines with generative AI tools for product recommendations. Most strikingly, 68% of consumers report comfort with AI-driven checkout – double the 34% figure from six months earlier.

Steel emphasized that preparation requires coordinated effort. “Neither merchants nor any entity affected should work in silos,” she advised. “Both the upfront detection and dispute resolution need to gear up to be ready.”

Best practices include working exclusively with registered AI agents and using tools to distinguish them from malicious bots. “This industry has spent billions, maybe trillions of dollars on finally getting good at detecting bots,” Steel noted. “But now we have to be okay with some of them.”

The Bottom Line: Embrace Data and Dialogue 

Both speakers agreed that, as rising chargeback volumes and agentic commerce reshape transaction patterns, merchants who invest in education, data collection, automation, and strategic dispute management will be best positioned to protect their revenue.

“Good quality data is extremely important as the chargeback rules are based on data and not feelings,” Steel concluded. “Acquirers and merchants should always concentrate on the power of the data and scrutinize what they are sending as compelling evidence.”

Houri finished up by emphasizing the importance of ongoing dialogue. “Let’s talk between the merchants and the acquirers,” he urged. “Let’s maintain knowledge of the rules. Let’s challenge each other genuinely to get a better ecosystem collectively. I think we are all together in this.”

The post Chargeback Strategies for the AI Era: Insights from Experts at Mastercard and JP Morgan appeared first on Justt.

Brian Davis, Host, The House of Fraud

Barbara Redaelli, Fraud & Dispute Strategy Manager, Lastminute.com

Dave Gestaut, Principal Technical Program Manager, Microsoft

Roenen Ben-Ami (moderator), Co-Founder and Chief Risk Officer, Justt

Key Takeaways

• Chargebacks are now a strategic lever for cost control and customer experience, if you treat them with nuance rather than a monolith labeled “fraud.”
• Post-COVID shifts (especially in travel) mean service-related disputes now dominate many merchants’ mixes; which means that preventing fraud alone won’t contain chargebacks.
• Modern programs blend deflection, prevention, and scaled representment with better data access/normalization and ML/AI to learn from issuer feedback at volume.
• Regulation and network rule changes (e.g., Visa programs and fee/timeline updates) reward speed, accuracy, and automation; slow processes now carry real cost.
• Cross-functional alignment (payments, CX, product, finance) is the unlock: trace chargebacks back to business decisions, pricing, billing, policies, not just fraud signals.

Introduction

The discussion, hosted live at ChargebackX, the first conference dedicated exclusively to the world of chargebacks, brought together four experts from across the payments ecosystem to share their experiences developing and leading chargeback programs across a variety of industries.

This session debated whether chargebacks are still the overlooked “black sheep” of payments, or if they’ve gained recognition as a critical part of the payments practice.

From “black sheep” to business lever

Ronen opened with an observation that many in the payments ecosystem can probably relate to: over the past decade, anti-fraud tooling leapt ahead, yet the way merchants handle chargebacks still feels stuck in the past. Manual, slow, and oddly invisible, chargebacks were treated as a cost of doing business. But now all of a sudden, that’s changing fast. 

Each panelist entered the space at a different time and from different industries, but they’ve converged on the same reality: chargebacks can no longer be ignored or viewed only through the lens of fraud. They’re a systemic problem that can point to issues throughout the company, from supply chain and fulfillment to customer service. And yes, fraud too.

The shift is cultural as much as technical. At large enterprises, teams now invite chargeback leaders into roadmap conversations because they’ve started to see the twin upside, lower cost and higher customer satisfaction, when disputes are handled proactively.

The #1 Misconception: “All Chargebacks Are Fraud”

Dave Gestaut, Principal Technical Program Manager at Microsoft, called out the biggest mindset trap: treating everything chargeback-related as fraud. That flattens the nuance that makes or breaks dispute outcomes. For one, “fraud” itself is complicated. It splits into third-party and first-party (malicious, opportunistic, or confusion-driven). Then there are merchant/service disputes (billing clarity, cancellations, delivery, policy friction), which are not always tied to fraud.

Here’s the real example he gave: A business line saw a spike and assumed fraud was the culprit. But the real root cause was a recent price increase and refund/cancel friction, a customer experience problem, not a fraud ring. The fix would have to do with improving communications and creating more intuitive customer flows, not tweaking the risk models.

Barbara Redaelli, Fraud & Dispute Strategy Manager at LastMinute.com, added another hard truth: “If you’re right, you’ll win” is not a strategy. While it’s idealistic to think each case will be reviewed thoroughly and completely, reviewers only have a couple minutes to scan your chargeback dispute packet. You need to explain your business, evidence, and entitlement clearly, as if they’ve never heard of you. There is no room for assumptions here.

Travel’s new reality: service disputes eclipse fraud

Pre-COVID, LastMinute.com saw a fraud-heavy mix of chargebacks. Today, however, roughly 60% are service-related, a reflection of how consumer expectations shifted during the pandemic. When travel restrictions and uncertainty made refunds commonplace, customers grew used to instant, flexible resolution. At the same time, overwhelmed support centers trained consumers to seek faster outcomes through their banks. Easier online dispute tools and stronger consumer protections reinforced that habit, while lingering mistrust from canceled trips and fine-print frustrations eroded confidence in merchant fairness. As a result, many customers now see filing a dispute not as fraud, but as standard customer service.

Travel, specifically, has two structural challenges:

1. Intangibility: You “deliver” a service, not a package. Evidence must prove service fulfillment (usage, boarding, or consumption), not just shipment. In many cases, the customer cannot preview the service beforehand, leading to potential gaps in expectations, like discovering upon arrival that your “small group” wine tour includes 25 people and a bus, not quite the intimate experience you imagined.
2. Long lead times: Chargeback clocks for service disputes often start at service date, not purchase. A February trip booked the prior May is still in the risk window long after authorization. That complicates accruals and operational planning.

Add multi-acquirer complexity and supplier dependencies (airline bankruptcies, refund policies), and you get a sector where data completeness and normalization are table stakes.

A much-needed strategy change: using data, automation, and AI

The House of Fraud Host Brian Davis’ inflection point came when his team realized that manually fighting chargebacks on low-price subscriptions cost more than it recovered. The labor and data prep simply didn’t pencil out. Early automation tools flipped the math. Suddenly, it became viable to contest high volumes of small-value disputes efficiently. Since then, the model has evolved from outsourced, manual rebuttals to automated, data-rich, template-aware representment, plus proactive alerts and deflection (e.g., near-real-time cancellation/refund paths that stop disputes before they’re filed).

Dave Gestaut from Microsoft highlighted the step-function: with hundreds of thousands of cases, LLM-powered analysis can mine issuer loss reasons and longform notes at scale, something infeasible for humans. The payoff is tighter templates and faster learning loops without replacing people; it supercharges them and in many cases produces better representments and better outcomes.

Data is the moat, and the bottleneck

As chargeback operations evolve from manual workflows to automated, AI-assisted systems, data becomes both the competitive advantage and the constraint. Barbara Redaelli from LastMinute.com highlighted two of the biggest blockers standing in the way:

• Accessibility: Acquirers deliver TC40s, chargeback data, and reason codes across SFTP, APIs, emails, mailboxes, or not at all. It’s difficult to access in one place and make sense of.
• Normalization: Every acquirer speaks a different dialect (fields, formats, reason-code mapping). Without unifying those streams, you’ll never see a trustworthy picture of liability, win rate, fees, or root causes.

Her solution is “translation: invest early in a data pipeline that standardizes inputs and enriches evidence. Everything else, deflection, prevention, rebuttal – depends on it.

Prevention vs. representment: run it as a flywheel

Too often, teams treat prevention and representment as separate tracks. In reality, the strongest programs run them as a flywheel, where each motion strengthens the next. Here’s how that looks:

1. Deflect: Eliminate avoidable disputes by solving billing confusion upfront, clear descriptors, instant receipts, click-to-cancel flows, and self-serve refunds where appropriate.
2. Prevent: Tune fraud controls and business policies to reduce friction without inviting abuse. Monitor leading indicators, as Brian Davis from The House of Fraud noted, even macro shifts like sudden currency devaluation can predict regional chargeback spikes.
3. Represent at scale: Use automation and machine learning to build consistent, data-backed cases, using a tool such as Justt. Role-specific templates and narrative support improve accuracy without slowing volume.
4. Learn: Feed issuer feedback and loss reasons back into CX, product, policy, and risk. Every dispute teaches you something about expectation gaps or friction points.

When this loop runs smoothly, you don’t just fight chargebacks better, you learn from each to prevent the next.

Rules and fees: timelines are tightening (and time is money)

Visa and Mastercard’s latest updates, stricter program thresholds, higher fees, and shorter response windows, are raising the bar for dispute management. The message is clear: move faster or pay for it. Manual workflows that once passed as “good enough” now cost real money in missed deadlines and compliance fees. Merchants that automate documentation, tracking, and evidence submission are saving time and protecting margin.

From cost of doing business to cross-functional discipline

Brian Davis from The House of Fraud contrasted the past’s “write it off” culture with today’s best-in-class programs that map chargebacks to specific product and policy drivers (free trials, returns, billing changes, global pricing, translation/localization). That specificity unlocks targeted fixes and credibility with finance and product.

Dave Gestaut’s barometer at Microsoft: teams now proactively invite chargeback leaders to discuss trends and roadmaps. That’s the signal you’re no longer the black sheep. You’re now part of core business hygiene.

A practical blueprint to take with you

• Own the taxonomy: Segment by third-party fraud, first-party fraud (malicious/opportunistic/confusion), and merchant/service issues.
• Instrument CX: Tighten descriptors, receipts, cancellation and refund flows. Monitor friction and post-purchase tickets.
• Normalize the data: Centralize acquirer feeds; standardize fields; reconcile fees and outcomes.
• Automate the middle: Alerts/deflection; template-driven representment; ML-assisted packet creation; SLA dashboards for clocks.
• Close the loop: Analyze issuer loss reasons (structured + unstructured), push learnings to product, pricing, and policy owners.
• Forecast the weird stuff: Macros (currency moves), policy shifts (pricing), and seasonality, so finance can accrue and ops can staff.

Ready to Reduce Chargebacks?

Most teams still treat chargebacks like the “black sheep” of payments, something to push into the fraud bucket and forget. But that no longer works. To truly reduce losses, you need to connect the dots across CX, payments, and risk, powered by a unified data pipeline and an always-on flywheel: deflect, prevent, represent, learn.

Justt brings the automation, analytics, and expertise to make that shift real.

See Justt in action →

FAQs

How can a cardholder file a chargeback months after purchase?

For service disputes, network timelines typically start at the service date, not the purchase date. In travel, a ticket bought in May for a February trip remains within the dispute window long after authorization. Plan accruals and evidence retention accordingly.

What’s the difference between third-party fraud and first-party fraud?

• Third-party fraud: an unauthorized party used the card.
• First-party fraud: the cardholder (or household) authorized the transaction but disputes it, maliciously, opportunistically (e.g., “I forgot my other account”), or due to confusion (billing descriptor, subscription renewals).

We’re “right”, why did we still lose?

Issuers review quickly. If your packet doesn’t teach a stranger what you sell, why the customer is liable, and how you delivered the service, supported by clear, labeled evidence, you can lose a technically correct case. Narrative and formatting matter.

What are the fastest ways to deflect avoidable disputes?

Clarify statement descriptors, send real receipts with item/service details, enable click-to-cancel, expose self-serve refunds where policy allows, and surface renewal notices and price changes clearly.

Which KPIs should we track beyond “win rate”?

• Dispute rate by type (fraud vs. service/merchant)
• Deflection rate (alerts resolved without chargeback)
• Timeliness (response-in-time %, cycle times)
• Net recovery (wins minus fees/ops cost)
• Root-cause mix (policy, pricing, CX, payments auth)
• Issuer feedback themes (structured + unstructured)

Our dispute mix shifted to service issues. What should we change first?

Audit policies and communications: cancellations, refunds, service delivery proof, SLAs, and customer messaging. Then tune representment templates to emphasize entitlement and fulfillment, not just identity.

How does AI help in representment?

LLMs can summarize issuer loss reasons at scale, suggest template improvements, and help assemble case narratives from raw logs. Pair that with human review, and you accelerate learning without sacrificing quality.

Multi-acquirer setup is messy. Any tips?

Centralize feeds, normalize fields, and standardize reason codes. Where acquirers lack APIs, schedule reliable SFTP/email ingestion. Build a single “source of truth” that powers deflection, case assembly, and reporting.

How do upcoming network fee/timeline changes affect us?

Shorter clocks + higher fees for misses mean you need automation, SLA monitoring, and clean evidence pipelines. Slow responses now show up directly as cost.

We’re early – what’s a pragmatic 90-day plan?

Stand up data intake/normalization, fix descriptors and receipts, deploy alerts/deflection, templatize top 5 reason codes, and build a weekly cross-functional review (payments, CX, support, finance) to drive root-cause fixes.

The post Are Chargebacks Still the Black Sheep of Payments, or Are They Finally Evolving? appeared first on Justt.

Each Justt customer has a different business with different pricing, product, and service dynamics, and, as thus, chargebacks impact them differently, resulting in differing chargeback strategies.

I had the lovely opportunity to discuss this on stage, at the MoneyTLV event, with:

Roni Siegmann – Director of Fraud Prevention at HoneyBook; and

Roie Shiloah – VP Revenue at Simply.

Moving away from the App Store has its challenges

Simply, formerly JoyTunes, is a privately held company that develops mobile applications for learning creative hobbies including music, drawing, and other creative skills.

Shiloah: “our core mission is to let people acquire creative skills in an easy and simple way. We started our journey as an application company where the core of the transaction happened in the app and through the App Store. In recent years we’ve shifted to the worlds of alternative billing.”

“As an app company that’s using App Stores—Google and Apple—you have peace of mind. On the one hand, you’re paying up to a 30% commission, but on the other hand, App Stores manage all the operations: chargebacks, refunds, and customer service.”

When the regulatory environment started changing, with Direct Merchant Accounts and lawsuits that forced app stores to allow alternative billing (e.g. the Epic Game lawsuit), Simply could begin to utilize alternative payments. It looked simple: “we decided to simply take Stripe’s SDK and implement it inside the app. Things looked great. Margins went up, but after several weeks, we began to discover things that we did not foresee.”

Simply started getting chargebacks—a lot of them. “The numbers initially just didn’t make sense. They were much higher than the benchmark, at 5-6% of transactions. Even worse, 80% of cases declared that the chargeback was related to fraud. We started getting fines.”

Shiloah understood that this required some serious attention: “we needed to understand the patterns. We’re an app company, we offer subscriptions, and we have free trials. Yet, users are filing chargebacks.”

From the data perspective, three things became apparent:

• The first insight was geographical: “some countries had very high chargeback rates. There were people there that used the trial, and when it ended, they submitted a chargeback. We needed to make a decision about what to do with those regions.”
• The second insight was that, in some cases, users did not seem to understand what a trial period means. “They assumed that the app was free and that someone should proactively contact them in order for them to be charged. In this case, we understood we needed to better communicate with the users—that we can no longer count on the app stores to do this job for us. This made us focus on the communication with the user: to reduce the friction they experienced and their likelihood to submit a chargeback.”
• The third insight was that, what consumers call fraud isn’t fraud in the ordinary sense, but rather, friendly fraud. There are users that are forgetful or that did not understand the situation, and then opted to act in the simplest way for them, which was to file a chargeback instead of accessing customer service and asking for a refund.

From the technology perspective, Shiloah understood Simply would have to begin using tools to deal with its chargebacks.

“Initially, we dealt with chargebacks on our own: we prepared the evidence, submitted, etc. We began to see the limit of that and to understand that there are services that can help us deal with this issue. We started working with Justt for chargeback management, and Verifi for alerts.”

Merchants of record need to deal with their merchants’ chargebacks

HoneyBook is an all-in-one client flow management platform designed for independent businesses, helping them handle the entire client process from lead inquiry to final payment. It centralizes tools like proposals, contracts, invoicing, online payments, and scheduling in one place—aiming to streamline operations, improve client experience, and allow businesses to focus on their work.

Roni Siegmann, Director of Fraud Prevention at HoneyBook, had unique challenges. “We are the Merchant of Record for all our merchants. Our customers get paid by their clients, such that our customers’ clients are the ones filing the chargebacks, and we need to deal with them. Some chargebacks are the result of fraud. In that case, our focus is prevention. But for the rest, we need to deal with disputing the chargeback. This is an important part of our offering at Honeybook—part of the service we give our merchants. When they get a chargeback, which is very distressing for them, we walk with them hand-in-hand. Those chargebacks are often their first and something they perceive as traumatic, where they feel that someone is trying to take away their hard-earned money. Merchants are very grateful, since it’s a difficult point for them, and this makes our service well-perceived by them.”

Siegmann needs to ensure that helping clients with chargebacks is an ROI-positive activity. “To do this, we work together with Justt and use their knowledge and ours to understand which chargebacks are worth fighting and which aren’t, and what the likelihood of winning is for each chargeback.”

Siegmann divides their strategy into two parts:

• Fraud needs to be dealt with almost immediately by preventing the transaction itself and not letting bad actors into our systems. “This is a challenge that never goes away, there always is fraud and you always need to be on top of it,” he says.
• For non-fraud chargebacks, he insists on understanding the entire case—what happened, is this friendly fraud, which evidence will help, and how to use data and AI to deal with the chargeback and dispute it.

When chargeback alerts matter and when they don’t

Setting up the ability to deal with customer service and payments has its costs, and Simply needed to incur them when it decided to use alternative payments. It could no longer have the App Stores do this work for them.

For Simply, dealing with chargebacks involves two tools:

• Justt for chargeback management and
• Verify for chargeback alerts.

For alerts, the approach is to understand the pattern behind the alert. If the alert shows there is a low likelihood of winning the chargeback, Simply will refund. But, if it looks like winning is likely, Simply will let the alert proceed to become a chargeback and dispute it.

Recent rule changes and seasonality impact chargeback strategies

Shiloah spoke about recent rule changes, such as the addition of the Stripe fee, and how they have impacted the company’s chargeback strategy. “In the case of Stripe, there is an additional charge of $15 if we fight a fraud chargeback and lose. This has impacted how we make decisions on what to fight and what not to fight.”

He explained that Simply’s approach to using alerts is also impacted by seasonality. “Sometimes, you can see that people made a New Year’s resolution to acquire a new creative skill. This, of course, means that in certain periods there is a higher chargeback risk.” This is taken into account with regards to alerts—following spikes, Simply may find it better to automatically refund based on the alert tool. While in typical times, Simply won’t automatically refund, but actually take a look at how people used the app prior to making the decision about whether to refund or not.

Siegman said that the cornerstone of their strategy is data: “the wiser you are, the better your wins are. We pick our fights and tell merchants what we believe about their ability to win, and we’re usually right.”

A new feature by Justt, Dispute Optimization, helps with determining the likelihood of winning a dispute, on a case-by-case basis, and to adjust decisions accordingly.

The post Honeybook and Simply – a tale of two different chargeback strategies appeared first on Justt.

Maisa Breve, Customer Operations Manager at Vio

Karlynn Ferris, Customer Resolution Manager at Sunbit

Will Plummer, CEO of Trust My Travel 

Dwayne Gefferie, Moderator

Key Takeaways

• Scale changes everything. Becoming merchant of record is the tipping point where chargeback compliance becomes mandatory.
• Speed plus evidence is the winning formula. Teams that submit complete evidence fast (same day/48 hours) see higher win rates.
• Friendly fraud is surging, especially in travel and U.S. cards; “buyer’s remorse” and issuer-driven disputes are common.
• Automation helps, but judgment matters. Tools streamline intake and documentation, while humans still triage edge cases and ensure evidence quality.
• Data is the backbone. Clear terms, rich checkout data, accurate descriptors, and audit trails are decisive in both prevention and representment.

Introduction

In this session from the inaugural ChargebackX conference, moderator Dwayne Jeffrey (payments strategist) spoke with three leaders who fight disputes at scale across very different models:

• Maisa Breve (Vio) manages customer operations for a hotel booking platform operating as both metasearch and merchant of record.
• Karlynn Ferris (Sunbit is responsible for customer resolution for a BNPL (Buy Now, Pay Later) provider that finances everyday needs (dental, veterinary, auto).
• Will Plummer (Trust My Travel)  leads a company that serves as a merchant of record and safeguarding solution for travel brands.

Despite coming from different industries and roles, their experience converges on a single theme: merchants need the right mix of automation, process discipline, and human review, grounded in great data, to navigate an evolving chargebacks ecosystem.

Chargebacks Are Now a Business-Critical Function

For many companies, the tipping point is becoming a merchant of record: disputes stop being a back-office task and become a core operating requirement. That’s what happened at Vio.com in 2021. Once Vio.com took on merchant of record responsibility, keeping scheme thresholds in check became non-negotiable. The team worked hard, worked with partners and tightened investigation processes to maintain healthy chargeback rates while scaling.

Sunbit reached a similar inflection in 2021, formalizing a dedicated chargeback team and optimizing for speed. With automation pre-filling case templates and packaging evidence, Sunbit aims to submit disputes the same day or within 48 hours, turning fast, complete filings into a repeatable business edge.

Trust My Travel brings a multi-vertical travel lens. Acting as merchant-of-record across different travel businesses, the team runs a data-first model: rule-based front-end controls (AVS/CVV/velocity) tuned by vertical and region; rich checkout data capture (e.g., travel dates, destination, cardholder location); and MID-per-sub-merchant plus concise descriptors to reduce “charge not recognized.” They also flag anomalies like unusual booking lead times. Even with strong tooling, a human sense-check decides which cases are truly winnable and ensures evidence maps exactly to the reason code, especially important in the U.S., where timelines are shorter, 3DS use is inconsistent, and issuers often tilt toward the cardholder.

Automation vs. Human Judgment

All three teams rely on automation to collect, normalize, and submit chargeback documents at scale. Yet each keeps humans in the loop for:

• Validating that evidence maps precisely to the reason code.
• Spotting mismatches across systems (e.g., booking data vs. processor data).
• Deciding when not to fight a case to avoid wasted cycles.

Each speaker attached different importance to how to deal with chargebacks:

• Sunbit emphasized workflow speed and completeness. 
• Trust My Travel emphasized merchant-specific context, while 
• Vio emphasized investigation and customer-centric resolutions even when the dispute isn’t strictly “legit.”

The Friendly Fraud Problem (and Why It’s Getting Worse)

Travel sees a pronounced rise in friendly fraud: customers who didn’t travel due to weather, schedule, or remorse, then call their bank rather than the merchant, sometimes after acknowledging non-refundable terms. Panelists observed that U.S. issuers frequently prompt or streamline disputes, making “charge not recognized” and “fraud” codes more common and harder to beat.

Post-pandemic behavior accelerated this. Consumers learned how easy bank-side dispute flows can be, especially via neobank portals. In contrast, many EU/UK banks still bury dispute paths deeper in their apps or websites, creating natural friction that reduces frivolous claims.

Vertical Patterns: Why Dental Sees More Than Auto Service

After establishing when chargebacks become business-critical for their respective companies and how regional dynamics differ, the panel turned to vertical-specific patterns. The contrasts are stark, especially inside BNPL. As Karlynn Ferris explained, dental is the industry that consistently sees the highest dispute rates across Sunbit’s portfolio, while auto service trends lower. 

Why dental Buy Now Pay Later spikes:

• Multi-visit, multi-procedure plans: Dental treatment often unfolds over multiple appointments, which creates ambiguity about timing, what was completed, and what remains.
• Insurance confusion: Patients frequently expect insurers to cover more than they do, leading to disputes tied to coverage misunderstandings.
• Higher ticket + remorse: Larger, multi-month plans can trigger second thoughts, e.g., a 12-month installment where the customer has already made five payments and then files a dispute.

Why auto service Buy Now Pay Later looks different:

• One-and-done: Even if several items are fixed in a single appointment, there’s less ongoing ambiguity, so fewer follow-up disputes about the service provided. As a result, there is also less room for confusion and remorse.

The point is: despite operating on the same BNPL rails, different industries can exhibit very different dispute signatures. Dental requires tighter consent flows, clearer plan/coverage communication, and meticulous documentation across visits; auto service benefits from the inherent clarity of a single service event.

The U.S. vs. International Divide

After discussing verticals, the conversation widened to geographical differences, and Will Plummer (Trust My Travel) put it clearly: the U.S. is the hardest place to fight disputes. Part of it is tempo; representment windows are tighter, so if your evidence isn’t assembled and ready fast, you’re already behind. 

Another part of it is posture; issuers are more inclined to side with the cardholder and, as Will noted, sometimes even prompt cardholders to dispute in the first place. Add inconsistent 3-D Secure usage and you get the familiar tradeoff: protect the transaction more aggressively and risk conversion, or keep checkout smooth and accept higher exposure on big-ticket travel.

The UK/EU picture feels steadier. Strong Customer Authentication and more consistent 3DS application create clearer rules of the road, and outcomes are correspondingly more predictable. It’s not friction-free. Teams still balance checkout experience against protection, but the rails themselves do more work. As Dwayne observed, many European banks have historically tucked dispute flows deeper in their apps (even as newer challengers surface them), which dampens the impulse to turn every misunderstanding into a chargeback.

In practice, Trust My Travel allocates more operational energy to the U.S., not because disputes are unwinnable, but because winning there depends on being both faster and cleaner: recognizable descriptors up front, tight evidence that maps precisely to the reason code, and the ability to move in hours, not days.

Prevent, Then Defend

The cheapest chargeback is the one that never happens. Across the panel, prevention looked like a handful of simple habits done relentlessly, and tuned by vertical and region.

1.  Design for recognition and consent (at checkout).
   • Make sure the buyer can see who is charging them. Trust My Travel shortens its own domain and appends the sub-merchant name in the descriptor (via per-MID setups) so “charge not recognized” is less likely.
   • Capture explicit acceptance. Sunbit’s flows collect each screen the customer saw plus a final signature/contract, which later anchors representment. In travel, Vio.com and Trust My Travel emphasize a visible T&Cs checkbox + timestamp for non-refundable terms.
   • Record context, not just card data. Travel dates, destination, cardholder location/device, and IP create a coherent story of intent you can defend later.

1. Calibrate risk at the edge (without stifling conversion).
   • Use AVS/CVV, velocity checks, and rule tolerances that match the business (tours vs. hotels vs. airlines; dental vs. auto). Trust My Travel varies thresholds by sub-merchant and even flags odd booking lead times (e.g., safaris bought two weeks out).
   • 3DS where it counts. In the UK/EU, strong SCA norms help. In the U.S., apply 3DS selectively (high ticket, cross-border, unusual patterns) to balance acceptance and protection.

1. Close the loop after purchase.
   • Receipts that restate the descriptor (“you’ll see this as…”) reduce “I don’t recognize it.”
   • Pre-billing nudges on first installments (BNPL) or upcoming travel dates jog memory and deflect avoidable disputes.
   • If a dispute alert lands, reach out fast; several panelists convert bank disputes into merchant-managed refunds/adjustments when there’s a genuine misunderstanding.

When a dispute does land, defend with discipline:

1. File fast, file complete. Sunbit targets same-day to 48 hours with cases that auto-include the application flow, signed contract, and payment schedule.
2. Map evidence to the reason code. Trust My Travel’s “sense-check” trims weak cases and ensures what you submit actually answers the issuer’s question.
3. Investigate edge cases. If a hotel claims “no record,” Vio.com validates with both sides; if the guest truly couldn’t stay, they refund or ask the cardholder to withdraw, faster and fairer than waiting out the cycle.
4. Tune by region. Expect tougher scrutiny (and shorter clocks) in the U.S.; in the UK/EU, SCA helps, but disciplined packets still decide close calls.

What we learned from the session was pairing best practices:

• Recognizable descriptors that are reinforced by post-purchase communications; 
• Checkout that proves intent (clear consent plus context) and not just authorization;
• A rapid representment cadence with reason-code-specific evidence; and, most importantly, using human judgment for triage and to ensure narrative coherence. 

Prevention keeps volumes down; process wins the cases you choose to fight. Teams that do both don’t just survive chargebacks, they scale through them, by improving their underlying business processes.

Tooling That Moves the Needle

Chargeback management tools don’t win cases on their own, but the right tool stack makes good process repeatable at scale. Here are the categories the panel relies on.

• Case assembly & submission platforms (e.g., Justt): centralize intake, auto-attach application flows and signatures, and standardize representments by reason code.
  
• Fraud/risk partners (e.g., Forter, per Vio.com): reduce bad transactions before they become disputes.
  
• Custom rule engines: per-vertical thresholds, MID-level monitoring, and anomaly flags (e.g., unusual booking lead times for safaris).
  

Across the board, no single tool solves it all. Winners orchestrate a stack, ensure evidence discipline, and keep humans focused on high-judgment calls.

AI: Where It Helps (and Where It Doesn’t. Yet)

Now: AI is valuable for pattern detection and scale analytics (e.g., surfacing anomaly clusters by merchant, product, geography, reason code). It frees people to focus on escalations and negotiation.

Not yet: Replacing human review in nuanced, customer-sensitive cases. Teams are cautious about automated outreach and “agentic” actions that could complicate authorization provenance and liability.

Final Takeaways from the Panel

• Data quality is destiny. If you can’t prove it, you won’t win it.
• Speed matters. Submitting complete evidence quickly improves outcomes.
• Design for recognition. Clean descriptors and proactive reminders reduce “I don’t recognize this” disputes.
• Tune by vertical and region. Dental ≠ auto; U.S. ≠ EU. Calibrate rules and evidence expectations accordingly.
• Fight smart. Don’t represent on hope, represent on proof.

Ready to Reduce Chargebacks?

This panel, spanning travel and BNPL, shows that prevention, speed, and evidence win. Justt operationalizes that with automation, data, and dispute experts built for scale. See Justt in action →

FAQs

Why are friendly fraud rates so high in travel?

Travel combines higher tickets, non-refundable terms, weather/schedule surprises, and remote fulfillment. Consumers often find it easier to call their bank than the merchant, especially in the U.S.

What’s the fastest way to reduce “charge not recognized”?

Use merchant-specific descriptors (or “on behalf of” phrasing), send post-purchase receipts that restate the descriptor, and consider pre-billing reminders with merchant name + amount + date.

What evidence wins BNPL disputes?

Signed application flows, explicit consent screens, IP/device stamps, the signed contract, and payment history (e.g., five successful months before a dispute) show recognition and intent.

How should a travel merchant handle “no record of reservation”?

Investigate with both hotel and customer immediately. If the hotel truly lacks the record, refund proactively (or ask the customer to withdraw the dispute and process the refund directly). Submit clear evidence if the reservation did exist (confirmation IDs, supplier logs, check-in data).

Why does dental see more disputes than auto service?

Multi-visit plans, complex insurance interactions, and higher totals create misunderstanding and remorse. Clear treatment plans, consent flows, and transparent financing schedules reduce downstream friction.

Does 3-D Secure fix this?

It helps, especially in the UK/EU, by shifting liability and strengthening authentication. In the U.S., inconsistent adoption limits its effect. Use 3DS selectively on higher-risk profiles to balance conversion and protection.

What should my team prioritize if we’re just getting started?

1. Centralize evidence collection; 
2. Template representments by reason code; 
3. Tighten descriptors & comms;
4. Instrument checkout to capture consent + context;
5. Measure speed to submit and iterate.

Where does Justt fit?

Justt consolidates intake, structures evidence to the right reason codes, and submits on your behalf, so your team can stay focused on prevention design, outreach, and high-judgment cases.

The post Chargebacks in the Real World appeared first on Justt.

Robbie MacDiarmid, VP, Consulting at CMSPI

Key Takeaways

• Every transaction requires consensus. A single “no” from any of the 6–8 parties in the payments chain can kill a sale, regardless of the merchant’s decision.
• Overly cautious risk settings cost merchants more revenue through lost approvals than actual fraud losses.
• Your inputs shape issuer trust. Pre-authorization screening builds long-term “credit health” with issuers and improves future approval rates.
• Measuring “approval rate” inconsistently across tools hides lost sales; the only true measure is your approval rate from the very top of the funnel to the very bottom.
• AI will blur the line between customers and bots. As agentic commerce grows, merchants must evolve risk models to avoid blocking legitimate AI-driven purchases.

Introduction

It’s one of the most common frustrations for any online merchant: your business is growing, your marketing is working, and customers want to buy your product, but you’re still losing sales. Legitimate customers are being declined, and it’s not always clear why. The assumption is that a payment is a simple exchange, but the reality is far more complex.

A single transaction is a journey through a supply chain where a cascade of decisions are made in milliseconds. At the recent inaugural ChargebackX conference, Robbie MacDiarmid, VP, Global Product Lead at CMSPI, presented a powerful framework for understanding this complexity. 

He posits that every payment decision is a balance between three competing forces: Fraud, Cost, and Approvals. Merchants often focus intensely on the fraud lever, but MacDiarmid’s insights reveal how this negatively impacts the approvals lever, ultimately driving up the effective cost through lost sales.

Here are four surprising truths from his session that reveal how an unbalanced payments strategy is costing you.

#1 – A Sale Requires Total Agreement, But a Decline Only Takes One “No”

For a transaction to be successful, every single partner in the payment chain must agree to it. This concept of “consensus” is fundamental. On average, a single transaction passes through the hands of six to eight different parties, from your payment gateway and fraud-screening tool to the card network and the customer’s issuing bank. Each one must give a “yes.”

“Every approved transaction in payments is the result of consensus. Every partner in the chain has to say yes in order for that transaction to be approved. So it’s full agreement and alignment that this transaction is legitimate. The flip side of that is it only takes one no for that sale to vanish.”

Read that again. It takes a “no” from just one party, out of the six to eight parties involved in the chain, to cancel the transaction.

This challenges the belief that the merchant has the final say. In reality, a merchant’s internal risk decision is just the first of many. As MacDiarmid notes, “The old adage you’re only as strong as your weakest link very much applies here. Although by weakest, we mean just whoever is the most risk averse in this case.” 

The final, effective policy is often set by the partner most focused on fraud, who can single-handedly veto a sale and override your desired approval rate.

#2 – The Hidden Cost of Caution is Higher Than the Cost of Fraud

Naturally, every merchant wants to eliminate fraud, but an overly cautious approach can be more damaging to the bottom line than the fraud it’s meant to prevent. 

False declines (legitimate customers who are incorrectly identified as risky and rejected) are estimated to cost businesses over $300 billion globally. That figure is higher than the cost of actual fraud losses! Worse, when a good customer is rejected, they might retry the purchase, but more likely than not, they’ll go to a competitor, or simply never make that purchase again, damaging your brand and future revenue.

According to a survey by the Merchant Risk Council, false declines account for a staggering 2% to 10% of all attempted transactions on a merchant’s site. This is the direct result of over-optimizing for the fraud lever at the expense of the approvals lever. While no one would argue against the importance of fighting fraud, an overly aggressive strategy turns away good customers and ultimately loses more revenue than the fraud itself would have cost.

#3 – Your Upstream Decisions Create Downstream Ripples

The choices a merchant makes before sending a transaction into the broader network significantly impact its final success rate. Issuing banks and card networks maintain a kind of “credit health” for each merchant ID (MID). Sending them cleaner, well-screened traffic builds trust and makes them less risk-averse toward your future transactions.

This concept is most tangible in the practice of “MID warming,” where new merchant IDs are slowly ramped up with low-volume, clean traffic to signal to issuers that they aren’t risky. The same principle applies to ongoing traffic. The data makes this clear: merchants who screen for fraud after authorization have, on average, a 5% lower authorization rate than those who use pre-authorization screening.

It’s worth double clicking on the insight here. Implementing robust pre-authorization fraud checks isn’t just about managing fraud at your own gate; it’s about conditioning the entire supply chain to improve the long-term health of your approvals.

#4: You’re Probably Measuring “Approval Rate” Incorrectly

Don’t worry; it’s not your fault. The term “approval rate” sounds simple, but different partners in the supply chain define it differently, leading to significant internal confusion. To understand the disconnect, let’s follow a short narrative of 100 customer purchase attempts.

Step 1: The Issuer 

The journey begins. The customer’s bank, the first major gate, declines 9 attempts for various reasons. From the issuer’s perspective, your approval rate is a solid 91%. But 9 sales are already gone.

Step 2: The Fraud Tool 

Your post-authorization fraud tool now sees the 91 approved transactions. It flags and declines 3 more. The tool proudly reports a 96.7% approval rate, but that’s 96.7% of a much smaller pie.

Step 3: The Reality 

When the dust settles, only 88 of the original 100 customers have succeeded. Your True Gross Approval Rate is 88%. This is the only number that reflects your bottom line.

This discrepancy matters immensely. Your fraud team might celebrate a 96.7% rate, focused on its slice of the fraud pie, while your finance team struggles to understand why 12% of potential sales are vanishing. Aligning on a “True Gross Approval Rate” is essential for accurately balancing fraud prevention with overall approvals. Once the True Gross Approval Rate has been calculated, the next step to getting closer to a real revenue impact is to calculate the approval rate across all payment methods and net of retries, both customer-initiated and merchant-initiated.

What Happens When You Can’t Tell a Customer From a Bot?

The payment ecosystem is a web of interconnected decisions. A merchant’s fraud strategy cannot operate in a silo; it must be holistic and account for the behavior of every partner in the chain. This has never been more critical as we enter the era of “Agentic Commerce,” where AI agents will automate purchases on behalf of consumers. 

The immediate challenge is that these AI agents will be nearly indistinguishable from the malicious bots that fraud systems have been designed for decades to block. But the deeper threat MacDiarmid highlights is the potential loss of critical data. Fraud systems rely on rich user and transaction signals, but with an AI agent making a purchase, you might lose access to all of it. The system will be starved of the very information it needs to make an informed decision.

This leaves every merchant with a crucial question to ponder: In a world where AI agents are expected to drive a $1.7 trillion market by 2030, how will your business adapt its risk strategy when your best customer starts to look like your biggest threat?

Interested in speaking with chargeback experts about your risk strategy? Schedule a demo today. 

FAQs

What does “consensus” mean in the payment chain?

It means every party involved, gateway, fraud tool, card network, and issuer, must agree to approve a transaction. A single “no” anywhere stops the sale, which makes alignment across partners crucial.

Why are false declines so costly?

Because they turn away legitimate customers who often don’t retry. Studies show false declines now cost more globally than actual fraud losses.

How can merchants improve their approval rates?

Start with upstream decisions using pre-authorization fraud screening, optimizing transaction data, and maintaining healthy merchant IDs, all of which build issuer confidence and increase long-term approval success.

What is the “True Gross Approval Rate”?

It’s the percentage of all customer purchase attempts that are successfully completed after every layer of screening. It reflects the real impact of fraud prevention policies on revenue.

How will AI and agentic commerce change risk management?

AI agents will soon make autonomous purchases, but they’ll resemble bots that fraud systems try to block. Merchants must adapt fraud models to recognize trusted AI agents while keeping real threats out.

The post Are You Risk Loving or Risk Averse? appeared first on Justt.

At ChargebackX 2025, Justt CEO and Co-founder, Ofir Tahor moderated a live panel with Jamie George (VP of Account Management and Partnerships at Ravelin) and Shahar Tal (CTO at Justt), on the practical realities of agentic payments. Their conversation produced a clear message: while agentic technology promises convenience and efficiency, it also introduces complex challenges around fraud, liability, and chargeback management that merchants must address sooner, rather than later. 

Watch the full webinar here, or read our highlights and key takeaways below:

The Current State of Agentic Commerce: Still WIP, but Evolving Fast 

Agentic AI refers to intelligent software capable of making decisions, interacting with digital environments, and taking autonomous actions. In ecommerce, this means AI agents can navigate websites, compare prices, and complete purchases based on simple instructions – saving consumers huge amounts of time while opening vast new commerce channels.

Despite widespread enthusiasm, current implementations reveal deployment roadblocks. During the webinar, Tal shared his experience using ChatGPT’s agentic mode to order fries from McDonald’s. The process took over 40 minutes, while the agent navigated captchas, switched between delivery platforms, and eventually succeeded. Similarly, George attempted booking hotels in Manhattan for his team, spending 45 minutes only to be offered a motel in Jersey City.

While AI is progressing at breakneck pace, it’s likely that online shopping infrastructure will also need to evolve to better support agentic transactions. Current storefronts are optimized for human conversion, not AI comprehension. “Storefronts were actually built to maximize conversion rates by taking all the fine print away from the funnel,” Tal explained. “But when you think about AI agents and LLMs, they need context in order to succeed, and this is something many storefronts today simply do not have.”

Major industry players are moving fast. While individual experiences remain clunky, tech giants are racing to establish standards and platforms. Google’s Agent Payment Protocol (AP2) attempts to solve one of agentic commerce’s biggest challenges: proving customer intent. The protocol provides frameworks for sharing purchase mandates through smart contracts, allowing merchants to demonstrate that customers authorized specific transactions. 

Similarly, Amazon’s Buy for Me feature (currently in beta) keeps customers within Amazon’s ecosystem while enabling an AI agent to purchase products they’re looking for, and which aren’t available on Amazon, from external merchants. The strategy maintains Amazon’s market dominance while expanding product availability. From a risk perspective, merchants participating in the program retain liability under their own terms and conditions – even though they receive minimal customer data and Amazon controls the entire transaction flow.

More Fraud is Coming

George’s prediction was unequivocal: “I am willing to place a bet with everyone here that fraud goes up for agentic commerce. I’ll give good odds on it.” Nobody took him up on these odds – the panel agreed that the rewards of agentic technology are matched by huge risks of true and friendly fraud. The reasons are numerous, including: 

• Fraud signal disruption: Traditional fraud signals like device fingerprints, IP geolocation, and behavioral analytics become unreliable when agents operate from virtual devices with cloud-based locations. This means that, among other counterfraud programs, Visa’s Compelling Evidence 3.0 requirements, which rely on matching IP addresses and device IDs to prove legitimate transactions, will not work in agentic environments.
• Agentic malware: As George noted, “The first people to adopt a new technology are always fraudsters. In a month, you will probably be able to go on the dark web and buy a bad agentic model”. These malware agents will likely be used to intercept or corrupt benign models, and even to carry out hyperintelligent, large-scale cyberattacks.
• Agentic Honeytraps: Bad actors may design scam websites to attract agentic ecommerce. These scam sites could potentially steal vast amounts of data and money from unsuspecting users.
• ATO Fraud: Account takeover fraud has long been a hacker mainstay, but it may soon become far more lucrative, as attackers could direct agents to conduct high-speed transactions in multiple locations at once. 
• Agents of friendly fraud chaos: Perhaps most pressingly, refund abuse and service-not-as-described friendly fraud chargebacks are likely to surge. While friendly fraud chargebacks already comprise 75% of all disputes, the added layer of abstraction in agentic commerce will provide bad actors with lucrative new ways of disputing legitimate transactions, causing this figure to rise. 

The Liability Question: Who Will Bear the Burden?

The question remains: when AI agents order the wrong colors, book incorrect hotels, or become tools for fraud, who will foot the bill? Unfortunately, it’s likely to be the merchant. 

“The card schemes aren’t going to take liability”, remarked George. “Neither are the customers, their issuers, or the AI model, who technically hasn’t made any money. That doesn’t leave many options – it’s going to be the merchant.”

The problem compounds when considering agent-to-agent transactions, which George described as “the most obvious case for Chinese whispers.” As requests pass through multiple AI interpreters, errors multiply, increasing the potential cost of liability for fulfillment failures.

This liability shift represents a paradigm change in the customer-merchant relationship. This is because the cardholder doesn’t see the merchant behind the transaction – as far as they’re concerned, the agent has “bugged out”. As Tal explained, “this makes it mentally easier to file chargebacks. We’re definitely going to see friendly fraud rise.”

Preparing for Agentic Commerce: Data and Automation Are Essential

The panel agreed that comprehensive data collection is the most effective defensive strategy at this stage. George noted that “going forward, making sure you get as much data as possible out of these agentic flows is going to be absolutely key.” Merchants should capture:

• Order context: items, prices, fulfillment timelines
• Execution signals: timestamps, session tokens, API headers, agent IDs
• Account-level information: customer history, login consistency, payment patterns

“It’s really a game of data,” Tal stressed. “Merchants need to choose partners that are really technological and can cope with the change. If you work manually or with outdated products that don’t have the ability to collect this data at scale, you need to rethink ASAP.”

Beyond data collection, merchants must embrace automation. Manual chargeback management systems cannot scale to handle the complexity and volume that agentic commerce will generate. “People would just not be able to cope with it,” Tal said. “AI created the problem, but AI also gave us tools to fight it.” 

As agentic commerce continues its inevitable rise, merchants should look to smart chargeback management systems that can automate evidence collection, representment creation, and submission – taking charge of this burden before it becomes overwhelming. However, merchants must also ensure that their chosen solution is adaptable enough to respond to the novel dispute scenarios that agentic commerce will generate. “Machine learning capabilities have never been so indispensable”, noted Tal. 

Read our previous blog post to learn more about agentic ecommerce and chargeback risk.

Timeline: Sooner Than You Think

While current agentic transaction volumes remain minimal, “fractions of a percent,” according to George, the trajectory suggests rapid acceleration. A 30% quarterly increase in AI tool usage, combined with growing consumer comfort with agent-driven purchases, indicates that widespread adoption could arrive within months rather than years.

“I don’t know how soon it will happen,” Tal concluded, “but I know that when it happens, it will happen very fast. Merchants really need to start to prepare for it immediately.”

George echoed this advice for staying current: “Keep close to all of the changes that are happening and be as dynamic as possible. If you think you’ve got your whole strategy nailed today, you’re probably going to be behind the curve.”

The post Mitigating Agentic Commerce Risk: Chargebacks and how you should prepare appeared first on Justt.

At the recent ChargebackX event, we had a chance to hear firsthand from merchants, acquirers, and other ecosystem participants about their initial experience with the program. Here is what we learned:

Confusion is (still) rife

From the sheer volume of VAMP-related questions we heard during the panels and Q&A sessions at ChargebackX – as well as the nature of some of these questions – we can infer that there’s still quite a bit of confusion about VAMP.

Even sophisticated and experienced payment teams were uncertain about issues such as:

• Will 3D Secure (3DS) transactions be exempted from TC40 rates? (The answer is “no, but using 3DS should reduce your overall fraud rate”.)
• What happens if you were on an early warning program in VDMP or VFMP? (The answer is that these programs are no longer live since April 1st, and that your VAMP ratio is calculated regardless of your performance in these programs.)

These are all nuanced topics, and it’s easy to get lost in said nuances. However, seeing as the program is already active, merchants should make a serious effort to plug any gaps they have in their understanding of the program. At this point there is a wealth of resources out there, for example:

• Visa’s fact sheet (PDF)
• Our previous articles on VAMP from May and July
• Justt’s Guide to Getting Ready for VAMP

These should probably cover most of your questions. Merchants are also welcome to address their questions to us – we will do our best to help you demystify what still remains unclear about VAMP.

Reminder: get in touch with your acquirer(s)!

We’ve mentioned previously that the new VAMP rules require you to actively communicate with the acquirer(s) or payment service provider(s) you work with. They have several important pieces of information which you cannot easily get elsewhere:

1. Whether you are currently above 30 basis points: Visa has granted all acquirers access to 15 months of data from the Visa analytics platform, which enables them to see any merchants that are above 30 basis points (with 220 being counted as ‘excessive’ for VAMP purposes). You should check whether your acquirer has opted into this program and how you can get access to these signals.
2. Your TC40 data: A TC40 report should be submitted by the issuer for every completed transaction where fraud has been claimed . Acquirers receive this data from Visa, so you should check with them how they can make it available to you (as the number of TC40s is essential for understanding the numerator of your VAMP ratio).
3. The acquirer’s own policies: It’s widely estimated, although, to the best of our knowledge, not yet confirmed, that some acquirers will look to apply their own limits to VAMP, as they will be looking to keep their merchant portfolio under VAMP ratios in order to avoid penalties. Different acquirers will likely develop different policies in this regard, and in some cases you might be able to negotiate specific terms. You should try to find out what your acquirers are planning as early as possible.

So let this be another reminder to go ahead and check in with your acquirers. You can also get in touch with Justt, or whichever chargeback management solution you’re partnering with, who might be able to communicate with the acquirer on your behalf.

Keeping ratios low is good for business – even if you’re below thresholds

Merchants who have less than 1500 TC40 (fraud reports) + TC15 (disputes) per month are excluded from VAMP, regardless of the ratio between this total and their overall transactions (TC05s). However, it’s still a good idea to monitor your VAMP ratios, and try to keep them as low as possible.

There are a few reasons for this. The obvious one is that your business might grow, and when that moment comes you do not want to be scrambling to fix issues with the risk of VAMP enrolment hanging over your head. On a more strategic level, a high VAMP ratio will typically indicate a systemic issue: mishandled fraud, issues with customer service, technical implementation problems, or sub-optimal use of pre-dispute tools. All these problems eat into your revenue – and fixing them will likely help you grow your business sustainably, regardless of Visa’s monitoring programs.

As we’ve highlighted before, data is one of the most important tools in your arsenal. Having a centralized view of your chargebacks – especially if you’re working with multiple PSPs and across different regions, customer segments, and product lines – will help you identify trends and pinpoint upstream issues that eventually manifest in your VAMP ratio (fraud, chargebacks). Make sure you collect the data, that you trust the data you collect, and that you have tools to easily gain insights from it.

We’re here to help.

We can’t speak for other chargeback management platforms, but at Justt we see our role as more than ‘win rate improvers’; rather, we are your revenue recovery partner. This includes helping you understand programs like VAMP and choosing the best strategies to reduce risk and potential losses that can stem from them.

If you have any questions about this topic or would like to learn more about optimizing your approach to chargeback mitigation, please don’t hesitate to get in touch or request a demo.

Watch the sessions from ChargebackX on Youtube

The post The State of VAMP in October 2025: What We Learned at ChargebackX appeared first on Justt.

After all, win rates record dispute reversals, which reflect revenue that was recovered – a no brainer, right? 

Unfortunately, it’s not so simple. While win rates are an important metric, they only tell half the story. They are important to track and measure but there’s more.

This article puts chargeback win rates under the microscope to show what they truly reveal, and what they leave out. Armed with this knowledge, you’ll be able to make dispute decisions that maximize revenue retention, rather than chasing imaginary numbers. 

For a deeper dive on the metrics that count, check out How to Calculate Your Net Dollar Recovery From Chargebacks.

How Chargeback Win Rates Can Distort Reality

Chargeback win rates are a simple ratio that measures a merchant’s percentage of chargebacks won, calculated as the number of successfully disputed chargebacks divided by the total number of chargebacks fought.

While the simplicity of this calculation makes it attractive for merchants and vendors alike, it also makes it easy to abuse. Here’s what to look out for:

Playing the Numbers

Conventional wisdom is that you want your win rate to be as high as possible. This is true in one sense (obviously it is better to win a dispute than to lose one), but with critical caveats. A very common fallacy is to only measure the win rates for those disputes you decided to fight, ignoring the fact that you may have chosen to NOT dispute large amounts of chargebacks, due to overcomplexity, certain reason codes, business lines, or low value. As you might expect, this strategy doesn’t shed much light on revenue retention at all, but simply obscures the true scale of your losses, because it only focuses on chargebacks you’ve decided to dispute. 

Chargeback teams (or external solutions) that rely on templates are often ill-equipped to deal with the nuanced disputes that arise from diverse business models, or those requiring highly specific evidence types to secure reversal. As a result, they might choose to sidestep certain types of chargebacks altogether by not putting up a fight. We’ve seen merchants doing this for 50-20% of their overall chargebacks. This can result in an inflated win rate that looks good on a slide deck, but doesn’t actually translate into optimal revenue recovery – as many of these disputes are actually winnable with more sophisticated chargeback solutions. 

Opportunity Cost 

Another cost often obscured by win rates is the cost of the chargeback management operation that’s needed to maintain them, which often requires significant human labor, which diverts employees  from where they could be more useful. 

When automation is limited to templates, merchants will often still need to invest time and effort that could have been spent elsewhere. Even if the system automates most ‘business as usual’ cases, chargebacks are unpredictable and surge prone – so when volumes soar, in-house teams become overwhelmed and miss deadlines, leaving chargebacks unrepresented. 

Outsourced chargeback mitigation can be almost as expensive as in-house teams. Many solutions charge monthly retainers that claim a large proportion of money saved – even if they only represent a fraction of winnable chargebacks. Others charge fees for every dispute represented, win or loss. If this amounts to a high percentage of your recovered revenue, then you aren’t really winning at all. 

Industry Variance

Merchants also need to consider that chargeback win rates vary between industries and business models. For instance, while luxury jewellers need to prioritize fighting every chargeback and must retain high win rates to succeed, SaaS vendors can operate under a different model. The same applies to BNPL merchants and more.

Playing for Keeps: Fighting Every Chargeback 

Conversely, if you fight every chargeback, your win rate is likely to be lower, but your revenue retention might be much higher – a 40% win rate on 99% of transactions is more valuable than a 99% win rate on 35% of transactions! 

A better way to calculate revenue retention from chargeback mitigation is by considering the value of your chargebacks won against the total cost of chargebacks lost and accepted, and that of your chargeback mitigation efforts. You can learn more here. 

Consider this example: a SaaS merchant sells subscriptions at $30 per month. They receive 300 chargebacks per month, of which they fight 99%, using a mitigation solution that costs $1,500 per month. They successfully reverse 60% of chargebacks fought. This means that the company faces $9,000 in disputed chargeback each month, of which they recover $5,346, which leaves $3,846 after the solution is paid for. That amounts to 42.7% of contested revenue recovered. 

Conversely, if the chargeback solution only fought 45% of the company’s more straightforward chargebacks, that might lead to a win rate of 75% – a great claim on paper! However, this would only translate into $3037, or 33% of revenue recovered. The moral of the story? You pay for dinner with revenue, not win rates, so you need to ensure that you’re fighting almost every chargeback! 

Of course, that’s only a rough example. Fortunately, a formula exists that allows you to work out exactly how much revenue you’re reclaiming on your chargebacks. This is it:

Net Dollar Recovery: The Informed Alternative to Win Rates

Net Dollar Recovery (NDR) is a formula for calculating the true value of your chargeback mitigation attempts. Instead of focusing on the partial picture that we get from win rates, this focuses on the total economic value that chargeback mitigation produces for your business.

To find your NDR, you’ll need the following pieces of information: 

Chargeback Volumes (Represented, Unrepresented, and Won)

• Total dollar value of your chargebacks over a given period
• Number of chargebacks received
• Percentage of chargebacks submitted
• Dispute win rate

Internal Costs

• Staff hours dedicated to chargebacks
• Average salary of in-house chargeback team member

Additional Chargeback Fees

• The cost of additional fees, fines, and penalties for disputes
• Fees for late submissions

Outsourced Solution Costs

• External solution fees

Read more about net dollar recovery 

Use Automation to Boost NDR, Win Rates, and More

Optimized chargeback management requires skilfully representing all of your disputes at as low a cost as possible. Of course, this begs the question: how do you balance the benefits of representing more chargebacks, with the high costs of in-house teams or manual outsourcing?

The answer? Automation.

Justt’s end-to-end automation removes manual work from the chargeback equation, so that evidence collection, representment writing, and submission happen instantly, cost-effectively, and with superhuman precision, using Justt’s dynamic arguments. This puts the solution in a unique position – Justt offers success-based pricing where you only pay for chargebacks won, so you can represent every dispute, knowing you’ll only pay when it pays off. 

Know When to Hold ‘Em

One caveat to all of the above is that with recent fee changes and other industry developments, there are situations where it becomes disadvantageous to fight a chargeback, and instead you should simply accept the chargeback and refund the transaction. This would typically be the case for low value transactions where the odds of winning are also low.  

This is the reasoning behind Justt’s Dispute Optimization. This recently-introduced feature carefully analyzes win-likelihood, transaction value, applicable fees, and available evidence to determine which chargebacks you should fight and which to refund for maximum NDR. You can choose to allow the feature to fully automate the decisioning process, or to simply make suggestions.

The main point here is that you are not forfeiting a chargeback simply because they are too complex for your team or your system to handle – these costs are virtually nil since Justt is fully automated. Rather, you are making an informed decision to avoid certain cases based on the costs and odds of winning them, in order to avoid potential fees from bodies such as Visa and Stripe.

Want to see what Justt can do for your NDR? Schedule a demo today. 

The post The Problem With Using Chargeback Win Rates as a KPI appeared first on Justt.