Fraud, Identity and Trust: Eric Kuvykin on Safer Payments for Small Business

By Eric Kuvykin. Review and new notebook example: October 17, 2026. Original publication date retained.

A fraud-review queue is difficult to judge when every blocked transaction is counted as a success. Some alerts identify a real problem, some interrupt legitimate business, and some remain unresolved. A useful review sheet keeps those outcomes separate instead of turning uncertainty into a favorable metric.

Give unresolved cases their own label

Imagine 100 fictional alerts. Reviewers later confirm 20 as fraudulent, 30 as legitimate and 50 as unresolved. Saying the system caught 100 fraud attempts is unsupported. Saying its false-positive rate is 30 percent is also misleading: 30 of 100 alerts is the observed legitimate share of this alert sample, not the false-positive rate across all legitimate transactions.

Even the 20 confirmed fraud cases do not reveal the system's recall, because this queue omits fraud that was never alerted. Keep the denominator written beside every percentage. Record when evidence was collected and allow an unresolved case to change status when later information arrives.

Describe evidence without collecting excess data

Use a case reference, alert reason, reviewer, review date, evidence source and final disposition. A customer's failure to answer a call should remain an unresolved contact outcome, not proof of fraud. An address mismatch is a signal to investigate under the provider's procedures, not a factual accusation about the customer.

For practice, include a legitimate repeat customer with a new device, an order with a mistyped address, a duplicate submission and a confirmed unauthorized transaction. These are invented training cases. Staff should explain what evidence distinguishes them and which actions require supervisor or provider approval. Do not put full card details or unnecessary personal documents into an informal spreadsheet.

Review queue quality alongside customer friction

Measure handling time, unanswered cases, repeated contacts and decisions changed after review. A shorter queue may mean better triage, but it may also mean cases were closed without evidence. Read a sample of closure notes before interpreting the count as improvement.

NIST's false-positive glossary illustrates the broader detection concept; this merchant worksheet is an operational recommendation, not a statistical validation or payment-network rule. Follow the actual processor's fraud and dispute procedures for live transactions.

For hypothetical teams in Florida, New York and New Jersey, use one set of outcome labels across locations so the results remain comparable. Continue through the AMS regional guides and Eric's payments and AI reading hub. A useful report makes uncertainty visible before anyone changes acceptance rules.