This article was first published on LinkedIn - follow us there for regular insights and updates on tech recruitment in US financial services: Trust in SODA

U.S. payments teams are moving beyond “fraud-only” AI toward agentic operations - software agents that read, decide, and act across dispute, risk, and back-office workflows under human oversight.

The payoff is faster cycle times, fewer manual touches, and better recovery rates without ripping out core systems.

Trust in SODA's engineering expert, Francis Alexander, explores the implications in more detail below. 

From models to agents in production

The pattern emerging across acquirers, PayFacs, and large processors is simple: start with narrowly scoped tasks, wire agents to existing case tools and CRMs, and enforce human review on exceptions. In dispute management, for example, agents now ingest documentation, classify reason codes, assemble evidence packets, and draft responses.

Analysts focus on edge cases instead of copying data between screens. The same approach is landing in reconciliation and exception handling, where agents auto-categorize ACH returns and prepare payment repair requests across rails.

Dispute automation goes mainstream.

AI is changing chargebacks by auto-assembling submission packets from shipping logs, CRM notes, device intelligence, and network tokens.

For U.S. merchants, especially mid-market programs without large dispute desks, this means shorter queues, fewer SLA breaches, and higher win rates.

Processors are also pushing friendly-fraud defenses earlier in the journey, surfacing richer purchase context to issuers and reducing avoidable chargebacks before they age into workload.

Behavioral intelligence rebundles fraud, scam, and ATO Defenses.

With deepfakes and scripted bots lifting attack volume, providers are fusing behavioral biometrics with transaction monitoring.

Instead of waiting for a transaction to trip static rules, models analyze how a user types, swipes, and navigates, plus whether a session “feels” automated or guided by a scammer.

When signals tip, agents pause the flow, request additional verification, or route to a specialist - often before funds leave the account.

Where Agentic Ops Land First – The Most Bankable U.S. Use Cases Right Now:

  • Drafting Reg E and chargeback responses from multi-system evidence.
  • Normalizing merchant descriptors and enriching records with tokens and IDs.
  • Reconciling ACH returns and exception items with auto-prepared memos.
  • Summarizing alerts for analysts, including recommended next actions and data provenance.

Teams report tangible handle-time reductions and fewer swivel-chair handoffs when these tasks are automated end-to-end with clear guardrails.

Governance catches up. U.S. regulators expect specificity in consumer communications and transparency around model use.

That means: keep model and prompt logs, document data sources and rights, and ensure humans can explain outcomes that affect customers. Align to widely used risk frameworks, establish model cards for every production use case, and implement drift monitoring so reviewers can see when performance or data mix changes.

Signals from the networks. Card networks and real-time payment operators are investing heavily in AI to streamline commerce and disrupt scams.

For issuers and acquirers, this creates leverage: connect to context-sharing services, adopt network-level dispute enhancements, and plug into consortium risk signals instead of trying to build all models from scratch. Smaller ISVs and PayFacs can federate capabilities through APIs while keeping their focus on merchant experience.

What top operators are doing now (U.S. focus)

1) Start with the “paper cuts.” Target disputes, refunds, and ACH/NACHA exceptions, where you can quantify touches per case. Automate intake (document and log ingestion), classification, and first-draft evidence; keep analysts in the loop for edge cases. Track auto-resolution rate, minutes to decision, and win-rate delta by reason code.

2) Upgrade scam defenses at initiation. Blend behavioral signals, device intelligence, and inbound-contact validation (voice/face spoofing checks) to reduce authorized scam losses as instant rails scale. Measure prevented outbound attempts and manual review deflection to prove ROI.

3) Industrialize model risk management. Map every agentic workflow to a risk lifecycle - govern, map, measure, manage. Produce model cards, prompt logs, data-provenance attestations, and drift monitors that can be retrieved in minutes during exams or partner due diligence.

4) Leverage data network effects - without boiling the ocean. Use network tokens, purchase-clarity services, and consortium models to enhance your own signals. Reserve in-house modeling for differentiators like vertical-specific fraud patterns or unique merchant telemetry.

Bottom line: agentic AI is no longer a lab experiment for U.S. payments. It’s a practical operating model - one that turns exception queues into straight-through outcomes, improves unit economics, and frees teams to focus on merchants, not rework.

Looking for Top Talent? 

If you're hoping to move into the top operators category, you'll need the right team by your side. Contact Francis directly if you need help building it: francis.alexander@trustinsoda.com