Card-Not-Present Fraud Detection

Card-not-present transactions include online, app, mail, or telephone purchases where a payment card is not physically presented. The absence of an in-person chip or swipe changes what information is available at authorization. Fraud controls may use transaction details, merchant and order information, device or session context, account history, and prior outcomes. Signals vary by payment network, issuer, merchant, and product. A risk model estimates the chance that an attempted transaction is unauthorized or otherwise problematic. A policy then decides whether to approve, decline, or request additional authentication. Authentication tools such as EMV 3-D Secure can exchange transaction and device context between merchants and issuers and may challenge some transactions. A challenge is not proof of fraud, and a frictionless result is not proof that a transaction is legitimate. Fraud labels are delayed and incomplete. A transaction may be reported days later, while a legitimate purchase can look unusual because a customer travels, changes devices, or makes a large purchase. Models should account for delayed chargebacks and confirmed outcomes, avoid leaking future information into training, and monitor changes in merchant mix and fraud patterns. Detection involves tradeoffs. Strict controls may reduce fraud but block legitimate customers; permissive controls may increase losses. Evaluate fraud capture, false declines, authentication completion, customer complaints, and loss severity. A single accuracy score is inadequate when fraud is rare and error costs differ. Payment data are sensitive. Limit access, protect device and account identifiers, and avoid retaining more data than necessary. Use human review for contested or high-impact cases. The model should assist a layered payment-security process that includes authentication, consumer support, dispute handling, and current network or jurisdiction rules.