AI Can Save Banks from Drowning in Fraud Disputes
By Gaurav Goyal, EXL
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Fraud disputes have reached critical mass.
The stakes: Each dispute already costs a bank $9–$10 to process — before any write-off or recovery loss — and the global cost of chargebacks alone is projected to reach $41.7 billion by 2028, according to Mastercard research.
Amid this mounting pressure, banks are balancing loss containment, customer trust, and tightening regulatory and network requirements to find a path forward.
Key insight: Unfortunately, legacy processes are creating a massive drag on that goal. Banks are drowning in manual reviews, sequential processing, human error and inconsistent decisions. The situation makes it harder for some banks to stay competitive.
Need to Know:
- Global chargeback volume is projected to reach 324 million by 2028 — up 24% from roughly 261 million in 2025.
- “Friendly fraud” — when the charge was approved but something else went wrong in the transaction — accounts for more than 45% of all chargebacks with losses projected near $5 billion over three years, per a 2025 report by Datos.
- Revised regulations are forcing banks to investigate and document this type of fraud under more heightened scrutiny and a much tighter timeline, making the margin of error razor thin.
Bottom line: Three forces are tightening at once, and each penalizes a manual, human-paced process more than the last.
Why Banks Must Find AI Solutions for Dispute Resolution
When it comes to the nature of disputes, the type of fraud growing the fastest happens to also be the hardest to judge.
The old question — “Was this authorized?” — has a clean answer and is typically the easiest type of dispute to manage. Between internet protocol address tracing, text, email or phone call prompts or payments to unusual merchants, unauthorized charges are usually easy to spot and quick to resolve.
The conundrum: But the fastest-rising cases of fraud aren’t so clear cut.
In these cases, payment was made legitimately but something went wrong. These scenarios, commonly called first-party, or “friendly” fraud, all arise with the customer. Examples include:
• A customer doesn’t recognize the merchant’s name on their credit card statement.
• A family member uses a credit card without the cardholder’s knowledge.
• A customer tries to get a refund under false pretenses, or dishonestly makes a claim that they didn’t receive a product when they actually did.
In each of those instances, banks have an onerous process that usually requires manual investigation and oversight, causing them to spend more time and resources than they would on a routine dispute.
Read more: Tiny Transactions May Be the Vanguard for Massive Payments Fraud
Regulations Tighten the Decisioning Timeline
Compounding the complexity, new regulations are shortening the timeline that banks have to work with to get answers.
For example, the Consumer Financial Protection Bureau’s Regulation E — which applies to point-of-sale transactions, ATM withdrawals or deposits, debit card purchases, phone or online banking transfers, person-to-person payments and gift card or prepaid cards – compresses the investigation window for a dispute.
Consumers have an incentive to report as fast as they can, with ascending liability caps kicking in at two days and 60 days, which forces banks to match that pace in decisioning. Regulation E can require provisional credit before all facts are known.
Meanwhile, Regulation Z — which applies to credit card transactions and often provides even stronger consumer protections — imposes fixed timelines for billing-error resolution and places heightened scrutiny around scam reimbursement. The regulation also provides an enhanced litmus test for decisioning consistency, explainability and documentation.
Key insight: As scrutiny increases under these rules, the pressure for banks to deliver faster, more accurate results also rises. So, where some executives may see regulation as an obstacle to automation, instead, it’s the strongest argument for it.
Read more: Four Ways Banks Can Turn Fraud Into a Loyalty Play
Fraud Can Be a Make-or-Break Moment for Customers
With every dispute, customer relationships are on the line. Consider that:
• The trust factor: 62% of consumers say that how their bank handled a dispute influenced their trust more than the fraud event itself.
• Winners win big: Nearly half (46%) of bank customers and 49% of credit card customers say they have a more positive impression of their bank or credit card issuer after experiencing an instance of fraud, and 92% of bank customers say they are likely to reuse their bank after experiencing a fraud issue and having it resolved.
• Banks are still falling short: Half (50%) of bank customers and 55% of credit card customers have not been prompted by their providers to act on security measures in the past 90 days, according to JD Power research.
Competitive insight: Slow, inconsistent dispute resolution is no longer just a line item; it is a central cause of customer attrition and it can drive customers right into the hands of a competitor. The cost of waiting on this issue is far greater than the price tag of integration.
Read more: Scams Are Driving a Wedge Between Banks and Customers
How to Create the Right AI Ecosystem
The problem is compounding, and as volumes rise and cases become more judgment-intensive, dispute management operations must become faster, more consistent and more auditable.
Tactical insight: The good news is that the technology is ready. Banks that re-invent the dispute lifecycle around AI-driven decisioning will improve speed, consistency, recovery and customer outcomes. The key is taking a measured, calculated approach to integration.
In order to ensure AI is put to its best use, banks must:
1. Build trust in AI: Banks need to start individual AI agents on high-volume tasks, establish trust and measurable value, and then integrate them into a broader system.
For example, AI agents would be used to assist analysts on small or discrete tasks — like culling through a credit card or bank statement for suspicious charges. By starting small, humans can easily check the AI agent’s work and start to trust that AI agents can execute and interpret the data properly.
2. Find a specialty: Once trust has been established, banks can have AI agents operate as a human specialist would, just in a way that supercharges efficiency.
In this phase, AI agents are used to execute most of a specific workflow under the supervision of a human to find errors and provide more high-level analysis. This step not only slashes manpower hours, but it opens up human analysts to think critically and not be bound by tedious and repetitive tasks.
3. Connect the specialists: As each agent builds up its own well-defined lane, a broader implementation can begin that connects these disparate functions to create an automated workflow.
What would this look like? One virtual agent may scan statements, while another verifies the location of a purchase, while another cross-references an audio transcript of the customer phone call to report the fraud. By linking these together, banks will have quicker and more accurate fraud decisioning.
4. Continue vigilance: As new regulations crop up and AI technology evolves, banks will need to constantly ensure the speed and accountability of their new AI processes are equal to the task the landscape now requires.
Internally, that means persistent human oversight will be needed, and any external partners banks bring in to help build out their automated workflows need to have the institutional knowledge to inform these updates.
The key for banks is to walk before they run. For early adopters, that may mean some trial and error on where AI can take over for human review and where it is still needed. But as more regulation is bound to crop up in the hopes of protecting both banks and consumers from fraud, it’s clear institutions must act now.
Read next: Fraud Is Inevitable. Cardholder Attrition Doesn’t Have to Be.
