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What is AI claim resolution (evidence-based claim resolution)?
AI claim resolution hands a specialised agent the claims whose outcome depends on a proof: it gets the evidence at the source, verifies it, applies the business rules and resolves the case in the existing tool — never refusing alone, escalating with the proof in hand when the case is doubtful.
An evidence-based claim is any request — refund, replacement, indemnity, repair, eligibility — whose outcome depends on a proof: photo, video, receipt, serial number. It is the class of cases classical automation does not close, because it breaks in three places: the proof is missing or unreadable (email round-trips), the proof is not verified (undue refunds, AI-generated images), or the rule is applied from memory (inconsistent decisions, indefensible in a dispute). AI claim resolution hands this treatment to an agent that chains four steps: get the right proof at the source, verify it (recapture, duplication or generated-image detection), apply the business rules, then resolve directly in the tool (CRM, ERP, ticket). Two principles distinguish it from a generic support agent: it is tool-agnostic — it plugs into the existing system rather than replacing it — and it keeps the human in the loop: the agent never refuses alone, it escalates with the proof when the stakes are high. According to SnapCall, one ticket in four carries an attachment, and those are the cases that cost the most and close the least.
Concrete example
A fashion marketplace handles an "item not as described" dispute. On receipt, the buyer photographs the defect from the return flow. The agent gets the proof from the requested angles, cross-references it with the seller's photos at intake, verifies that no image has been reused or generated, applies the arbitration policy, and decides: refund, return, or escalation to a human mediator if the proofs contradict each other. An arbitration that took several days and two or three exchanges is settled in a single interaction, with an auditable decision journal. Clear-cut cases close on their own; edge cases, and only those, go to a human — with the proof attached. On this kind of flow, SnapCall measures 46% faster resolution and +16% first-contact resolution.
Comparison
| Dimension | Generic support agent | Evidence-based claim resolution |
|---|---|---|
| Input handled | The ticket text | The proof obtained at the source (photo, video, document) |
| Proof verification | None | Recapture, duplication and AI-generated-image detection |
| Decision | A written reply | Business rules applied identically, decision journalled |
| Resolution | A suggestion to the human agent | The case resolved directly in the CRM or ERP |
| Role of the human | Handles everything | Decides the doubtful cases — the agent never refuses alone |
FAQ
What is evidence-based claim resolution?
It is the automated handling, by an AI agent, of requests whose outcome depends on a proof — refund, replacement, indemnity, warranty, eligibility. The agent gets the proof at the source, verifies its authenticity, applies the business rules and resolves the case in the existing tool, escalating doubtful cases to a human.
How is it different from a chatbot or a classic support agent?
A chatbot replies; a support agent reads the attachment that is already there. An evidence-based claim resolution agent gets the right proof, verifies its authenticity, applies your rules and closes the case in the CRM — never refusing alone, and without depending on any single tool.
Which claims are concerned?
Any request whose decision depends on a visual or documentary proof: damaged parcel, defective product, warranty defect, conformity dispute on a marketplace, traveller indemnity, or receipt-based refund.
How does the agent verify that a proof is authentic?
Through guided live capture from the requested angles, automatic recapture when the proof is missing, online duplication detection, AI-generated-image detection, cross-consistency checks between pieces, and a timestamped decision journal.
What should you require before deploying such an agent?
Five guarantees: a confidence threshold below which a human decides, a per-case decision journal, fraud verification of the proofs, a GDPR retention policy for customer visuals, and pricing per resolved case rather than per seat.
What measurable results can be expected?
On evidence-based claim flows, SnapCall measures up to 46% faster resolution and +16% first-contact resolution; recall that one support ticket in four carries an attachment.
See also
Further reading
SnapCall — evidence-based claim resolution (AI Claim Resolution)
Sources
- SnapCall — AI Claim Resolution. https://snapcall.io