Payment exception resolution
Investigate failed or held payments and propose a policy-compliant next action.
What this workflow does
Investigate failed or held payments and propose a policy-compliant next action. It separates observed facts from inference, surfaces conflicting evidence, and delivers a review-ready finding set instead of an opaque answer.
What you gain
Approved payment disposition and customer task
What the AI agent changes
How this worked before
Traditional automation could collect records, but a person still had to compare sources, resolve conflicts, and write the conclusion.
What the AI agent changes
The agent combines payment state, policy, prior attempts, and exception evidence to choose a permitted recovery path for approval.
Agent trigger
A payment fails, is held, or breaches an exception threshold.
Inputs
- Process context: payment event, account, fraud, sanctions, and ledger evidence
- Approved policies, ownership, and exception rules
Agent flow
- 1
Collect source material from the approved systems and record its timestamp.
- 2
Cross-check conflicting signals and separate facts from inference.
- 3
Prepare a permitted retry, return, hold, or escalation with citations, unknowns, and confidence.
- 4
Deliver the approved report and preserve its evidence set.
Human decisions
After step 3
The accountable process owner approves a permitted retry, return, hold, or escalation.
Outcome
- Approved payment disposition and customer task
- Evidence, exceptions, and audit trail
Guardrails
- Label inference separately from observed facts.
- Do not close the investigation while required sources are unavailable.
Risks and mitigations
Missing or stale evidence can produce a confident but incomplete finding.
Show source coverage, conflicts, timestamps, and unanswered questions.
Sources and evidence
Sources establish feasibility or impact. Not every metric comes from an identical implementation.
Anthropic
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