Chargebacks are a growing problem for merchants, and the manual processes that many rely on are no longer sufficient to keep up with the volume and complexity of disputes. In this article, we'll explore the different levels of chargeback automation, from basic productivity tools to advanced AI-powered solutions. We'll examine why traditional rule-based systems are no longer enough, and why AI is emerging as the key to truly scalable, effective chargeback management
While chargebacks are a big problem for merchants, automation is far from ubiquitous. Research by Riskified found that 59% of merchants manage their chargebacks completely manually. Even larger businesses might still be dependent on spreadsheets, email chains, manual evidence collection, and a constant scramble to meet dispute submission deadlines.
However, the move towards automation is inevitable, simply because the old way of doing things cannot keep pace.
More online transactions lead to more chargebacks. Credit card disputes surged at the start of the COVID-19 pandemic and have only increased since then; up to 75% of these disputes fall under the category of friendly fraud.Â
Handling higher dispute volumes manually is exceedingly difficult. Bandwidth limitations mean that many cases are simply not responded to at all, leaving significant money on the table; in other cases, response quality and consistency suffers due to human error. Attempting to keep up by constantly increasing headcount can lead to unreasonable costs. What’s more, chargeback volumes tend to fluctuate (e.g., they increase dramatically during the EoY shopping season) – making capacity planning an impossible choice between large teams that sit idle most of the time, or smaller teams that buckle under the increased caseload during surges.
At the same time, the financial impact of chargebacks is substantial. While many merchants keep their chargeback ratios below 1% of total transactions, when a chargeback does occur, the full amount of the sale is lost, not just the margin. As a result, chargebacks can account for 5-25% of net income – a massive hit to profitability at a time when many merchants are running on tight margins already.
It's clear that automation is needed. But as we shall see in the next section, this can mean different things to different people.
Automation is a broad concept. If your starting point is that you’re keeping all your evidence in folders and managing disputes in a spreadsheet, a workflow builder will go a long way towards automating your day-to-day drudgery. But newer technology, and especially recent developments in AI, offer an opportunity to take things many steps further.
From a merchant’s perspective, there are three distinct stages to a chargeback dispute – and different levels of automation can be introduced in each:
| Stage | Basic automation | Advanced automation |
| Evidence collection | PSP and merchant data | PSP, merchant data, 3rd party data enrichment |
| Dispute representment | Templates | Dynamic arguments |
| Representment submission | Automated submission and win/loss reporting | Continuous optimization and A/B testing to improve win/loss rates |
Evidence collection is the process of gathering all the relevant data about a disputed transaction. At a basic level, this means pulling data from your payment service provider and internal systems. A more advanced automation solution will also enrich this with information from third-party sources, such as additional information about the IP address, phone, email, or device, and behavioral data that can help prove the legitimacy of a transaction.
Dispute representment is where the actual chargeback response is composed. Many merchants rely on generic templates, perhaps automatically populated with transaction details. However, advanced solutions like Justt use dynamic arguments - tailored responses that are automatically generated for each unique case based on the specific evidence available, optimized for the relevant reason code, issuing bank, card network, and more. This allows for a much more compelling and targeted response.
Representment submission is the final step (not including cases that go to arbitration), where the assembled response is sent to the issuing bank and the outcome is recorded. At a basic level, automation can streamline the submission process itself and provide standardized reporting on win/loss rates. A more advanced solution runs continuous A/B testing, feeding the results data into machine learning models that improve in order to optimize the entire process and increase win rates over time.
Our vision with Justt was always to build a product that’s fully automated. Basic productivity-enhancers are great, but they don’t really solve the problems that most chargeback teams are actually struggling with since they require close human intervention at every stage. Quality will continue to suffer during surges, teams will still be overworked or understaffed, etc.; to solve these, we had to take the human (mostly) out of the loop. Pretty soon, we realized that this could only be achieved with AI.
The chargeback dispute process is complex, with hundreds of data points potentially impacting the outcome of each case. When you consider the myriad combinations of products, customer profiles, payment methods, reason codes, and representment strategies, it quickly becomes clear that simple templates or rule-based automations are inadequate to handle this complexity at scale.
A single large merchant could easily face hundreds of thousands of chargebacks per month, each one slightly different. Attempting to manually review and respond to each case would require an ever-growing army of trained specialist.
The complexity doesn’t end there: every payment service provider has different rules and fee structures, every industry has unique quirks, and different customer segments exhibit different behavior patterns. A fashion retailer serving younger consumers, for example, might see high rates of "item not as described" chargebacks, while a digital streaming service could face more "subscription not cancelled" cases. Trying to model every potential scenario in a static workflow would result in a tangled web of rules that quickly becomes unmanageable.
A truly effective chargeback solution needs to accomplish two key things.
This is where AI and machine learning truly shine. By analyzing the outcomes of thousands of past disputes and constantly testing different representment approaches, an AI-powered solution can achieve significantly better performance that is tailored to very specific combination of variables. It can adapt its strategies in real time based on subtle shifts in the data, continuously optimizing its performance in a way that rule-based systems simply cannot match.
As more and more financial activity moves online, the volume and complexity of chargebacks will only continue to grow. Merchants who try to keep up using legacy systems or human-dependent processes will increasingly struggle, risking lost revenue, higher operational costs, and ultimately, competitive disadvantage. The future of chargeback management belongs to AI - it's the only way to truly scale dispute resolution while continuously improving outcomes.
This shift is part of a broader trend in the payments and fintech space, where AI and machine learning are becoming essential tools for managing risk, preventing fraud, and optimizing the customer experience. From dynamic transaction risk scoring to intelligent payment routing and beyond, AI is transforming every aspect of the financial ecosystem.
Chargeback management is no exception - in fact, it's one of the areas where AI can deliver the most immediate and measurable impact. Merchants who embrace AI-powered automation will be best positioned to protect their revenue, streamline their operations, and stay ahead of the curve.
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