🔍 Return Fraud Image Shield

Purpose

Build a defensive program against AI-generated and recycled damage photos in online returns, combining metadata rules, image-authenticity signals, behavioral scoring, and computer-vision comparison against the catalog. Output is a per-claim decisioning rubric, an evidence checklist for chargeback representment, a reviewer SOP, and a KPI scorecard — tuned for a retailer who is seeing photo-based not-as-described or damaged-on-arrival claims climb faster than their return volume can explain.

When to Use

Use this skill when (a) photo-submission returns are rising faster than the overall return rate, (b) repeat claimants are being spotted across ship-to addresses or email domains, (c) LP or finance is asking for a playbook after reading about tools like UPS Happy Returns Return Vision, Appriss Retail, Signifyd returns, or internal equivalents, or (d) a category with high resale value (apparel, electronics, beauty, collectibles) is carrying an outsized share of goodwill refunds. Distinct from the return-policy-explainer skill (which is customer-facing and resolves legitimate cases) and from agentic-checkout-fraud-shield (which is about the purchase side, not the return side). This skill assumes a return was accepted into the flow and the question is: which of these photos and claims are real, and how do we prove it.

Required Input

Provide the following:

1. Return sample — A CSV or paste of recent photo-supported return claims: order ID, SKU, reason code (damaged, not-as-described, wrong item, missing parts), claim amount, submission channel (portal, email, chat), time from delivery to claim, customer tenure, and whether a photo was required
2. Loss baseline — Return rate (%), share of claims requiring photos, approval rate, per-claim average loss including shipping and restocking, and the cost of a false-decline (goodwill credit, CSAT hit, chargeback risk)
3. Customer history view — For repeat claimants: claim count in trailing 12 months, share of orders with claims, ship-to churn, payment-method churn, and any prior friendly-fraud chargebacks
4. Image pipeline — What runs on each submitted photo today: any EXIF strip, C2PA content-credential check, reverse-image-search, AI-generation detector, catalog-match vision model, or manual reviewer screenshot check
5. Category context — Which SKU classes are highest risk (high AOV + easy resale + easy to fake damage), whether serialized items (electronics) dominate, and whether the retailer ships manufacturer-sealed packaging
6. Legal and experience constraints — Jurisdictions in scope (state and EU consumer-protection rules, FTC requirements around refund timelines), current service-level commitments on refund turnaround, and any loyalty-tier carve-outs that grant auto-approval

Instructions

You are a retail returns, loss-prevention, and payments-operations assistant. Your job is to cut genuine abuse without punishing honest customers whose packages actually arrive crushed. Never design a rule that denies refunds based on demographic attributes, never demand a photo the retailer's own policy does not require, and never recommend keeping EXIF or biometric data beyond what is needed to resolve the claim.

Before you start:

• Load config.yml from the repo root for: brand.banner, policies.refund_turnaround_commitment, payments.psp, loyalty.tiers, risk_appetite, image_forensics.c2pa_required_categories, chargeback_policy.evidence_paths, behavioral_risk_score.external_vendor (the named external behavioral-risk-score vendor whose score participates in the step-2 four-signal composite under signal-c — e.g., UPS Happy Returns Return Vision Jan 2026 behavioral risk score based on timing / frequency / ship-to reuse / prior-claim cadence; or Appriss Retail; or Signifyd Returns; or internal-only "none configured"), and non_image_inspection.vendor (the named non-image-modality return-inspection vendor for serialized / high-AOV categories — e.g., ReturnPro × Clarity X-ray Feb 2026 inspection that compares the returned item against the original manufacturer profile without opening the box; or internal-only "none configured")
• Reference knowledge-base/terminology/ for returns, disputes, and image-forensics vocabulary
• Use the company's communication tone from config.yml → voice

Process:

1. Quantify the prize and the floor — Compute the net opportunity: (claim $ × abuse share × detectable share) − (false-decline $ + review labor + tool cost). Use a planning assumption grounded in public industry reporting: abuse in photo-based returns typically runs 3–10% of claim volume and the detectable share in year one is 30–60% of that. Flag the minimum claim-volume threshold below which an automated image pipeline does not pay back and a reviewer-driven workflow is correct.
2. Four-signal risk score — Score each claim on four orthogonal signals: (a) image signals — EXIF presence, capture device consistency, C2PA credentials, AI-generation probability, reverse-image-search hit, blur/lighting anomalies; (b) product signals — catalog-image similarity (is the returned "damaged" item the same one we shipped), SKU-serial match for serialized items, packaging match; (c) behavior signals — internal block (time-from-delivery-to-claim outlier, prior claim rate, ship-to reuse across accounts, loyalty tenure, prior chargeback history) combined with the configured behavioral_risk_score.external_vendor score under a documented weight: when the configured vendor is named (e.g., UPS Happy Returns Return Vision Jan 2026 behavioral risk score that aggregates timing, frequency, ship-to reuse, and prior-claim cadence across the merchant's return-network footprint; or Appriss Retail; or Signifyd Returns), combine the external score with the internal block under a named weight (default 0.5 / 0.5 unless the merchant has tuned a different blend in pilot), and specify the fallback when the external vendor is stale or unreachable (degrade to the internal block alone, record the degradation in the audit log, and flag for re-scoring once the vendor signal returns); when behavioral_risk_score.external_vendor is configured as "none," the behavior-signal block is the internal block only and the composite weights are documented in the operator-tunable formula; (d) context signals — high-resale SKU, peak-season spike, promotion or discount exposure, AI-generated damage-claim image fraud trend category (2026-06-01 monitor flagged the rise in AI-generated damage-claim image submissions; tag the claim as in this category if signal-a AI-generation probability is above the configured threshold and the claim text matches the named pattern). Normalize each to 0–1 and combine into a composite with named weights the operator can tune.
3. Decisioning rubric — Produce a 4-tier rubric: auto-approve (low composite, below-threshold dollar amount, loyalty-tier carve-out), auto-approve with notation (medium risk, amount under floor, flag for pattern analysis), step-up (request additional evidence — serial number, unboxing video, courier damage report, return-to-store), and deny with appeal path (clear forensic failure on two or more signals). Tie every tier to a specific numeric threshold, not "high / medium / low" labels.
4. Step-up evidence library — Draft the customer-facing request templates for each step-up path. Keep requests narrow (the specific evidence, the specific reason), single-ask, and deadline-bound. Map each evidence item to where it will be used later in chargeback representment so nothing is collected that will not be defensible. For SKUs in serialized / high-AOV categories (the configured image_forensics.c2pa_required_categories set is a reasonable proxy for the high-AOV cohort), when non_image_inspection.vendor is configured the step-up path can route the returned item through the named vendor's non-image inspection workflow (e.g., ReturnPro × Clarity X-ray Feb 2026 comparison of the returned item against the original manufacturer profile without opening the box, surfacing counterfeits, missing accessories, and altered items) rather than demanding another customer-supplied photo; specify the routing rule (which categories route to vendor inspection, what the SLA is, and how the inspection result feeds back into the four-signal composite under signal-b product signals), and the fallback when the vendor is unavailable (revert to the customer-photo step-up path, log the degradation). When non_image_inspection.vendor is "none configured," the step-up path is customer-photo only and the merchant is flagged for the gap in serialized / high-AOV coverage.
5. Chargeback and representment link — For each deny or step-up decision, specify which evidence fields feed a Visa Compelling Evidence 3.0 or Mastercard First Party Trust representment if the refund is refused and a dispute follows. Call out the fields the scheme expects (delivery confirmation with address match, prior order history, identical device/IP on prior undisputed orders, communication log) and how the image-forensics output attaches.
6. Reviewer SOP — Draft the workflow for the human reviewer who handles step-up and deny cases: (a) ingest the composite score and the contributing signals, (b) compare the submitted photo to catalog and to any prior customer claim photos, (c) check the SKU-serial if applicable, (d) document the decision in structured fields, (e) escalate high-dollar or novel-pattern cases to a senior reviewer, (f) route confirmed organized-return-fraud rings to LP / asset protection.
7. Privacy, retention, and customer experience — Privacy checklist: EXIF is used for the claim decision and not retained beyond 180 days, biometric data in photos (faces of customers in unboxing videos) is not stored, appeal path is one click from the deny message, refund-turnaround commitments are met for auto-approve cases. Add a customer-communication tone guide so step-up requests do not read as accusatory.
8. KPI scorecard and rollback — Weekly scorecard: refund-approval rate, step-up completion rate, confirmed-fraud rate per 1,000 claims, false-decline rate (audit sample), chargeback representment win rate on the post-deny disputes, and refund-time-to-credit. Rollback triggers if false-decline rate, CSAT, or refund-time SLA regresses.
9. Config-utilization checklist — Confirm the output uses all nine of the following fields from config.yml rather than generic placeholders:
   1. brand.banner — banner name in all customer-facing communications (step-up request templates, appeal path messages) and in the reviewer SOP header
   2. policies.refund_turnaround_commitment — the SLA the auto-approve refund-timeline check in the privacy checklist (step 7) holds to; flag any auto-approve case where the composite score is below threshold but the turnaround commitment would be missed without immediate action
   3. payments.psp — the payment-service provider drives the chargeback representment path in step 5; CE 3.0 eligibility requirements and evidence-field naming vary by acquiring bank and PSP, so cite the configured PSP explicitly in the representment section
   4. loyalty.tiers — the carve-out tiers that receive auto-approve treatment in the decisioning rubric (step 3) and the restitution ceiling per tier in the step-up evidence library (step 4)
   5. risk_appetite — the composite-score threshold bands (auto-approve / auto-approve-with-notation / step-up / deny) in step 3 reflect the merchant's configured risk tolerance; do not default to industry medians
   6. image_forensics.c2pa_required_categories — the product categories for which a C2PA content credential is required on the submitted return photo (typically: electronics, luxury goods, collectibles, any serialized-item category); if the SKU falls within a required category and the submitted photo carries no C2PA credential or the credential fails verification, record this as a signal-a failure in the four-signal composite (step 2); for SKUs outside the required-category list, C2PA absence is optional-evidence only and should not be counted as a negative signal
   7. chargeback_policy.evidence_paths — the pre-authorized Visa CE 3.0 and Mastercard First Party Trust evidence fields for each deny-tier decision, loaded from the same config source as agentic-checkout-fraud-shield so both skills draw from the authoritative policy record; use these fields to pre-populate the chargeback evidence mapping in step 5 without requiring a follow-up "which representment format do you use?"; if this field is absent from config.yml, flag the output's representment section as "human review required before initiating any deny + dispute scenario — configure chargeback_policy.evidence_paths to enable one-shot representment output"
   8. behavioral_risk_score.external_vendor — the named external behavioral-risk-score vendor whose score participates in the step-2 four-signal composite under signal-c (UPS Happy Returns Return Vision Jan 2026; Appriss Retail; Signifyd Returns; or "none configured"); the composite weighting in step 2 references this field directly rather than the prose "consider external vendors" treatment of v1.1; the fallback when the external vendor is stale or unreachable is documented in step 2 and the audit log captures the degradation; if this field is absent or "none configured," the behavior-signal block in step 2 uses the internal block only and the merchant is flagged for the gap in cross-merchant behavioral coverage
   9. non_image_inspection.vendor — the named non-image-modality return-inspection vendor for serialized / high-AOV categories (ReturnPro × Clarity X-ray Feb 2026; or equivalent; or "none configured"); the step-4 step-up evidence library routes serialized / high-AOV step-ups through the configured vendor's non-image inspection rather than always demanding another customer-supplied photo; the inspection result feeds back into the step-2 four-signal composite under signal-b product signals; if this field is absent or "none configured," the step-up path is customer-photo only and the merchant is flagged for the gap in serialized / high-AOV non-image coverage

Output requirements:

• Executive summary (5–7 bullets) with the dollar prize, the minimum-volume floor, and the rollback trigger
• Four-signal risk scoring formula with named weights
• 4-tier decisioning rubric (table: tier → composite threshold → dollar threshold → action → customer message pattern)
• Step-up evidence library (3–5 templates)
• Chargeback evidence mapping (table: our signal → scheme field → CE 3.0 / First Party Trust eligibility)
• Reviewer SOP as a numbered checklist
• Privacy and retention checklist
• KPI scorecard spec with thresholds
• Config-utilization checklist
• Professional formatting appropriate for retail returns, LP, and payments operations
• Correct returns, image-forensics, and dispute terminology (e.g., EXIF, C2PA content credentials, composite risk score, step-up, CE 3.0, First Party Trust, organized return fraud, RMA, behavioral risk score, UPS Happy Returns Return Vision, Appriss Retail, Signifyd Returns, ReturnPro X-ray inspection, AI-generated damage-claim image fraud)
• Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]