Chargeback analysts spend their day chasing evidence under deadline pressure. See how AI agents assemble evidence packs and resolve disputes faster.
Every morning in the chargeback department starts with a flood of new disputes, each with missing information, strict deadlines, and pressure to get it right.
Chargebacks are a deep use case for Zamp in banking, not the boundary of what the platform does. The same case-ingestion-and-escalation architecture runs freight-order settlement across 50+ vendor portals and enterprise-wide deployments in life sciences. Full story: why Zamp isn't just a finance AI company.
Analysts jump between portals, chase data from merchants and banks, and piece together evidence packs, all under constant audit pressure.
By evening, there’s still a backlog. The next day, the cycle repeats. It’s no surprise that burnout is common and accuracy is hard to sustain at scale.
This is the reality of chargeback teams today. The numbers aren’t helping: Mastercard projects 324 million chargebacks a year by 2028, a 24% jump from today. Most teams are still doing the same swivel-chair work they’ve done for years.
In the instance of a fraud spike, these teams don’t have any respite. The fact that the tech stack doesn’t provide them with useful analytics just adds to their everyday woes.
Workflow-based automations have historically struggled with chargebacks because the work is complex and highly nuanced. Every dispute raises unique challenges that a workflow can’t process, requiring human judgment.
Evidence comes in emails, PDFs, and screenshots, making verification difficult for rigid systems. Chargeback codes, deadlines, and dispute formats change frequently, and this is where rule-based systems fail to keep up.
Even when rules are clear—for example, Reason code 13.2 requires delivery proof—that proof might be buried in a PDF or an email attachment. The bot stops, and the chargeback analyst steps in.
Add to this integration nightmares and data silos, and everything is back to manual.
AI agents are built differently. They combine language understanding, reasoning, and action to handle real-world tasks the way humans do.
AI agents are your digital chargeback analysts who can read documents, grasp context, and act across multiple systems, without relying on fixed workflows.
AI agents don’t just automate a task; they understand intent, adapt to change, and keep humans fully in the loop. They can:
AI agents are compliant by design and compliance-first, built from the ground up to align with regulations and governed by controls that ensure every action is auditable, secure, and policy-compliant.
That looks like:
Financial institutions at the forefront of agentic AI adoption have seen the following benefits:
To introduce AI agents safely and effectively:
AI agents give chargeback teams the extra hands and brains they need to keep up with volume, accuracy, and compliance, without burning out.
It’s the first real step toward end-to-end, intelligent chargeback automation that actually works.
