Mastercard Excessive Fraud Merchant Program: Merchant Risk and Remediation

Reconcile the fraud-monitoring population

Start with the account or merchant scope named in the notice and rebuild the relevant period from internal payment data. Keep counts, sales volume, dispute or fraud reports available to the merchant, and the date each event entered the reporting view. Timing differences are common enough that a merchant should understand them before escalating a discrepancy.

Separate the monitoring dataset from representment outcomes. A dispute win can be useful for case operations while the fraud-monitoring program may use a different event definition or timing rule.

Segment risk before changing acceptance rules

Break the exposure down by product, order value, acquisition channel, country or region, device/account history, shipping pattern, and authentication or authorization signals the merchant lawfully retains. A blended account average can hide one small segment that creates most of the problem.

Avoid responding to a monitoring notice by simply declining more transactions across the board. Measure false positives and legitimate approval loss alongside fraud and dispute outcomes so a remediation does not reduce one metric by creating an unnecessary conversion problem elsewhere.

Connect fraud controls to post-transaction outcomes

For major rule changes, record what condition triggered review or decline and compare later fraud, chargeback, refund, and customer-support outcomes. That link helps the team identify which controls are predictive and which merely add friction.

Technical signals such as AVS, CVV, IP, device, velocity, or prior account activity should be treated according to their actual evidentiary role. No single match should be turned into an unsupported identity conclusion.

Own remediation and evidence retention separately

Create one workstream for fraud prevention and another for evidence quality. Better dispute evidence does not prevent a fraudulent purchase, and tighter fraud controls do not repair missing logs after a case arrives. Both can matter to account health, but they solve different failures.

Assign an owner and measurement date to each control change. Preserve the before-and-after rule configuration or decision logic at a level that lets the merchant explain what changed without exposing sensitive fraud rules publicly.

Use the current program notice for status decisions

Thresholds, calculations, and remediation requirements should be checked against the merchant’s current Mastercard or processor notice rather than copied from a static page. Keep dated notices so finance, risk, and leadership are working from the same program state.

The durable internal process is to reconcile the metric, segment the contributing activity, assign targeted controls, and measure whether the next reporting periods improve.

Example: one acquisition channel drives the fraud concentration

A merchant's aggregate fraud rate rises sharply, but segmentation shows most problematic transactions come from one paid-acquisition campaign feeding a specific product and checkout path. Blocking all customers or tightening every rule would create unnecessary false declines.

Break the affected population down by traffic source, product, geography, authentication path, device/risk signals, and fulfillment pattern where volume permits. Remediation should target the source of the fraud concentration and be measured against both fraud reduction and legitimate approval impact.

Link excessive-fraud remediation to the transaction cohorts that created the problem

Fraud-program review should start with the acquirer's reported fraud population and then map those events back to merchant transactions. Capture transaction date, channel, authentication, product, account age, device or network signals used internally, shipping or service destination, acquisition source, and fraud outcome. The objective is to find concentration. A portfolio-wide fraud rate can hide one affiliate, one country, one high-risk SKU, or one attack window that drives most losses.

Avoid fixing fraud by simply declining far more legitimate customers. Segment the offending cohort and test controls against approval and conversion. Bot or enumeration attacks may require velocity, device, IP reputation, or payment-attempt controls; account takeover may require authentication and account-change protections; stolen-card ecommerce may require stronger risk scoring or 3DS. Each intervention should target the observed pattern and have a measurable expected effect.

Connect pre-transaction and post-transaction data. A fraud rule can lower approvals but still fail if bad orders that pass are fulfilled immediately. Add fulfillment holds for selected high-risk transactions where appropriate, alert integration, or manual review. Track chargebacks and confirmed fraud back to the original risk decision so the model or rules can learn from outcomes. Dispute evidence retention is a separate workstream from fraud prevention but should use the same transaction identifiers.

Formal status should come from current Mastercard/acquirer documentation and notices. Preserve program correspondence and deadlines, assign an executive owner, and document remediation. Do not manipulate merchant names or transaction routing to obscure performance. A credible recovery plan reduces the fraudulent transaction population while maintaining an auditable record of the controls and results.

Watch enumeration and card-testing indicators separately from completed-payment fraud

Some attacks create huge volumes of authorization attempts without many settled transactions. Track failed attempts, velocity, BIN concentration, IP/device patterns, and small-amount testing separately from completed fraud. A fraud remediation plan focused only on chargebacks can miss the attack traffic that is degrading the payment environment.

Coordinate gateway, WAF, bot-management, and payment controls so enumeration is blocked as early as possible. Preserve enough event data to measure the attack without retaining unnecessary sensitive card information.

Review fraud-control changes for false-positive customer harm

Tightening risk rules during remediation can increase legitimate declines, account locks, or support friction. Track approval rate, manual-review volume, customer complaints, and conversion alongside fraud reduction. If a control blocks a broad population to stop a narrow attack, refine it. Sustainable fraud remediation reduces malicious activity without creating a new source of customer disputes or abandoning profitable, low-risk customers.

Fraud-program remediation should be evaluated for customer harm as well as fraud reduction. A rule that blocks large amounts of legitimate traffic may lower one risk metric while creating cancellations, support complaints, and lost revenue. Track rule hit rate, confirmed fraud, manual-review outcomes, false positives, authentication results, geography, product, and merchant ID. When controls change, compare cohorts before and after rather than judging from aggregate fraud alone. Also coordinate with the acquirer or processor on the current program status and required remediation evidence. The goal is a measurable reduction in fraudulent activity without replacing one account problem with an indiscriminate acceptance policy that damages legitimate customers.