Why Banks Are Rethinking Human Review in Dispute Operations
By Liz Froment, Contributor at The Financial Brand
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Banks have been using AI to flag suspicious transactions and score fraud risk for years. Those systems help prioritize work but still depend on human decision-making to move cases forward. Yet, fraud tactics continue to evolve, consumers have less patience for slow dispute resolution and chargeback volume keeps climbing, with global totals projected to reach 324 million by 2028 — a 2.7x increase over six years.
That combination is straining dispute operations. Manual reviews struggle to scale as transaction volume increases, regulatory requirements tighten and consumer expectations rise. Backlogs and delays increase operational costs and put customer trust at risk.
To address these pressures, financial institutions are turning to agentic AI. Agentic systems operate autonomously. Instead of flagging anomalies and stopping, agents can process disputes by gathering evidence, applying policy and acting within clearly defined boundaries.
The move toward agentic AI forces banks to decide where autonomy makes sense, what guardrails are needed and which dispute processes can move beyond human review without increasing risk.
Need to Know:
- Dispute operations are straining under rising fraud complexity, tighter regulations and surging chargebacks — projected to reach 324 million globally by 2028.
- Agentic AI moves beyond flagging risk, autonomously gathering evidence, applying policy and resolving routine disputes without waiting for human review.
- The automation line isn’t dollar value — it’s signal quality: high-confidence, repeatable cases are the best candidates for full autonomy.
- Early adopters are seeing real impact, including faster resolutions, 20–30% lower fraud losses and tens of millions in operating savings — when governance is done right.
- Guardrails determine success: clearly defined autonomy, continuous monitoring, explainability and human escalation where judgment and regulation still matter.
From Flagging Risk to Making Decisions
“Traditional AI relies on deterministic rules and static models. It is limited to doing what it was told to do ahead of time,” Craig Agulnek, VP, product management, at Quavo, an automated dispute management solutions provider for financial institutions, told The Financial Brand. “Agentic AI is different. With minimal human input, it can decide what to do next based on what it sees. It is not limited to a single path and can take multiple steps in parallel, adjusting as it runs.”
The difference shows up in dispute processes. Rather than flagging a transaction and pausing for human intervention, agentic systems continue gathering context. They review related transactions, look at recent spending behavior, pull in additional data sources and assess evidence against dispute rules before determining the next action. Some transactions are resolved automatically, while others are held for human review.
“The difference is architectural. Traditional AI operates as a sophisticated advisor. It scores, recommends and highlights things for attention. But the decision stays with a person,” says Husnain Bajwa, SVP of risk solutions at SEON, a fraud prevention firm. “Agentic AI moves the decision boundary itself. The system doesn’t just assess a case. It reasons about it, acts on it and closes the loop.”
Agentic AI moves routine dispute decisions through the system, rather than leaving them waiting in the workflow until a human has the time to begin the next steps.
“The real innovation isn’t removing humans. Most dispute decisions aren’t actually judgment calls. They’re pattern-matching exercises where human involvement adds latency without adding insight,” Bajwa says. “A duplicate billing dispute with matching device fingerprints and consistent behavioral signals? That’s not a hard problem. It’s a slow one when you put a person in the middle of it.”
This shift leaves banks with a decision to make. Some disputes can move without human review. Others still require it. The challenge for banks is deciding which cases fall into each category.
Where Autonomy Makes Sense — and Where It Doesn’t
Many banks still determine automation based solely on transaction value. High-dollar disputes go to human review and low-dollar disputes get automated. But that approach doesn’t always work.
“The mistake most institutions make is drawing the line by dollar amount or customer segment rather than by signal quality,” Bajwa says. “A platinum customer’s clear-cut duplicate charge doesn’t need human review any more than anyone else’s. What matters is whether your data stack can generate high-confidence decisions.”
Dollar amounts may not matter as much as signal clarity. Automation works best when dispute patterns are well understood and repeatable. These disputes tend to resolve without issue when the available evidence points to a clear outcome.
“Dispute processes that are best suited for full automation are high-volume, repetitive and low risk,” Agulnek says. “True fraud cases where transaction patterns clearly match known fraud vectors are ideal for automation, allowing institutions to fast-track provisional credit and resolution.”
Routine notifications, such as status updates or informational messages, can also be automated, removing manual steps without changing how disputes are decided.
Human review is still critical, especially when data clarity breaks down. “This type of agentic automation should be paired with a robust escalation human review layer,” Joe Robinson, co-founder and CEO at Hummingbird, a risk and compliance platform, told The Financial Brand. “Fully automating certain processes is most valuable from a risk mitigation standpoint when it is the other side of the coin to workflows that prioritize human judgment and discretion.”
KPMG reports that institutions using automated dispute workflows have achieved straight-through digital claims processing rates of 50% to 60%, reduced fraud losses by 20% to 30%, cut resolution times and saved more than $20 million in operating expenses. And McKinsey predicts agentic AI could deliver a 20x productivity potential, especially in areas primed for AI, such as fighting financial crime.
Automation reduces manual dispute volume and frees human teams to focus on cases that require regulatory judgment, discretion or higher-touch customer service.
Implementing Guardrails and Governance
Financial institutions that deploy autonomous systems need clear boundaries to comply with regulations. That means defining what agentic AI can decide, what data it can access and what actions it can take without human approval.
The stakes are high. Two-thirds of customers say they’d consider switching banks after a slow or frustrating dispute experience, while 73% say positive dispute handling influences loyalty. That makes guardrails a customer issue as much as a risk mitigation tool. Poor decisions, delays, or unclear outcomes can create compliance issues and put customer relationships at risk.
“Guardrails start with governance,” says Agulnek. “Banks create cross-functional committees to define acceptable use cases and set limits. Decisions are not made in a vacuum or by one team; they are consistent across the entire organization.”
Those limits can determine how far autonomy goes. Bajwa describes this as an operational design domain, which defines the system’s operational boundaries. “You define the envelope within which the system operates independently. Transaction types, dollar thresholds, confidence scores, customer segments and regulatory jurisdictions. The system acts freely inside that envelope and escalates at the boundaries.”
For those systems to hold up under scrutiny, autonomous decisions must be explainable. “Banks need visibility and explainability into the ‘why’ behind agentic AI decisions,” says Agulnek. “When a system acts, banks want to understand not only what information was used but also how it was used.”
That means clear audit trails showing what information was used and how policies were applied, so outcomes can be reviewed by internal risk teams, auditors, or regulators.
However, guardrails aren’t static and need to be monitored and updated continuously. Agentic AI governance can’t be a set-it-and-forget-it system. “The biggest red flag we see with AI implementation, agentic or otherwise, is thinking that it’s ‘done’ once it reaches a production environment,” Robinson says. “AI tools require continued attention and maintenance. With AI in particular, ongoing tuning is essential.”
What’s Next for Banks
Agentic AI changes how disputes are processed. Routine decisions don’t need to wait on human review, while complex or high-risk cases still require judgment and oversight.
The banks that benefit will be the ones that are clear about where automation applies, where it stops and who is accountable when it does. Getting that right determines whether automation helps or creates new problems.
