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Use Case
Returns & Refund Fraud Detection
Score returns and refunds for fraud risk; route high-risk cases for manual review.
Value chainCustomer ServiceObjectiveMitigate RiskAI typeModelKPIRefund leakage −30%Value£210KFeasibilityHigh
Dependencies
Returns, Customer, Order, Sales
Feasibility
High — based on current data & integration readiness
Recommendation
🔬 Pilot candidate — high feasibility, validate with a focused trial
AI deployment flow
How signal turns into outcome for this use case.
Insight
Score returns and refunds for fraud risk; route high-risk cases for manual review.
Business Objective
Mitigate Risk
Data Required
Returns, Customer, Order, Sales
AI
Model — Returns & Refund Fraud Detection
Decision
KPI: Refund leakage −30%
Action
Customer Service systems
Outcome
£210K value
Business outcomes
- Primary KPIRefund leakage −30%
- Estimated annual value£210K
- Strategic objectiveMitigate Risk
- Value chain stageCustomer Service
Type of AI
ModelPredictive or generative model that scores, forecasts or generates content.
Action surfaces
Customer Service systems
Data sources required
Unified domains powering this use case via Xfuze.
Returns
ServiceReturn reasons, refund flows and fraud signals across web, store and 3PL.
Typical sources
OMSWMSPOS3PL
Customer
CustomerUnified golden record of every known shopper across channels, identities and households.
Typical sources
CDPCRMLoyaltyEcommercePOS
Order
Sales & CommerceFulfilment lifecycle of every order — promised dates, allocations, splits and exceptions.
Typical sources
OMSWMSTMSCarrier APIs
Sales
Sales & CommerceEvery transaction line across stores, web, marketplace and wholesale — the canonical ledger.
Typical sources
POSEcommerceMarketplacesOMS
Related use cases
Same value chain or business objective.