Chargebacks and Friendly Fraud

A chargeback occurs when a cardholder disputes a transaction through their issuer and the payment network process may reverse funds while the case is reviewed. Reasons can include unauthorized use, a billing error, non-delivery, duplicate billing, or dissatisfaction with a purchase. Merchants may call some disputes “friendly fraud” when they believe the cardholder received the goods or services, but that term does not prove intent; customers can be confused, forget a purchase, or have a legitimate complaint. Machine-learning systems may estimate which transactions are more likely to result in a dispute or identify patterns for review. Inputs can include order details, transaction history, fulfillment events, customer communications, and past dispute outcomes. A risk score can help prioritize documentation, but labels may be incomplete: chargebacks may arrive late, outcomes vary by dispute reason, and merchants may not see the full issuer context. A responsible workflow separates detection from the decision to contest a charge. Collect relevant evidence such as delivery confirmation, refund history, product description, and communication, then follow the payment network and applicable legal process. Do not fabricate evidence or automatically deny a customer's report because a model flags the account. Provide a clear support channel and correct billing mistakes promptly. Evaluate chargeback models by dispute reason and customer impact, not only overall accuracy. A false positive can lead to an unfair account block or an inappropriate representment. Track win rates, time to resolve, complaints, refunds, and legitimate orders declined. Review performance across purchase channels and customer groups. Use the term “friendly fraud” cautiously in customer-facing decisions. A model estimates patterns; it cannot determine a person's intent from transaction data alone. Human review, accurate records, and current dispute rules remain essential.