Commercial Payments Fraud Glossary

Analytics models that continuously adjust based on new information, user behaviors, and emerging risk patterns.

The use of artificial intelligence, machine learning, and advanced analytics to identify fraud indicators, anomalies, and patterns that may be difficult to detect through traditional fraud monitoring tools alone, helping organizations detect threats at greater speed and scale.

The process of identifying activity that deviates from expected behavior and may indicate fraud or elevated risk.

The analysis of user behavior patterns, such as login activity, navigation habits, and payment actions, to identify behavior that may indicate fraud.

The practice of enriching fraud detection and investigation efforts with additional data sources, behavioral data, and external risk signals to support decision-making.

Analytical techniques that use historical and current data to identify patterns and assess the likelihood of future fraud events.

Specific characteristics, behaviors, or events that may suggest increased fraud risk.

When a fraudster gains unauthorized access to a legitimate user account and uses it to initiate or manipulate payments.

The analysis of user behavior patterns, such as login activity, navigation habits, and payment activity, to identify suspicious behavior that may indicate fraud.

A fraud scheme in which criminals impersonate a trusted individual or organization through email to convince a victim to send funds or sensitive information, so that they may infiltrate systems and/or carry out various schemes.

A legitimate payment or activity incorrectly identified as suspicious.

The process of identifying activity, behavior, or transactions that may be fraudulent before financial loss occurs.

The continuous analysis of customer activity, payment behavior, and transaction data to identify potential fraud risks.

A bank account used to receive, transfer, or hide funds obtained through fraudulent activity. The account holder may knowingly or unknowingly assist the fraudster.

A calculated value that indicates the likelihood that a payment or activity is fraudulent.

The use of deception or manipulation to persuade individuals to reveal information, grant access, or authorize fraudulent payments. Many BEC attacks rely on social engineering tactics.

User behavior, account activity, or payment actions that warrant additional review due to elevated fraud risk.

A fraud scheme in which criminals combine legitimate and fabricated information to create a false identity which they then use to open accounts or conduct financial crimes.

The review of payment transactions for anomalies, risk indicators, or suspicious behavior.

The process of adjusting fraud detection models, rules, and thresholds to improve accuracy, reduce false positives, and align detection strategies with evolving risks.

The coordination of multiple fraud detection tools, intelligence sources, and workflows to support more consistent and efficient fraud decisions.

The end-to-end flow of a payment from initiation through approval, processing, settlement, and, when necessary, investigation.

A condition that occurs when fraud teams receive large volumes of alerts, making it more difficult to quickly identify and act on genuine threats.

The process of organizing, investigating, documenting, and resolving fraud alerts and incidents.

The act of stopping, delaying, reviewing, or escalating potentially fraudulent activity before funds are released or transactions are completed.

A centralized workspace where fraud analysts review alerts, investigate suspicious activity, determine next steps, and manage fraud cases.

The ability to identify suspicious activity as it occurs, enabling earlier intervention and faster response.

An additional layer of identity verification, such as a one-time passcode, biometric verification or security challenge, triggered when suspicious activity or elevated risk is detected.

The actions a user performs within a system or application, such as logins, navigation patterns, entitlement changes, payment-related activity, and other interactions.

The ability to track, recreate, and “see” a user's digital activity in a particular solution or app to better understand the events leading up to a potentially fraudulent action.

User actions and behaviors that occur before a payment is initiated, such as logins, device activity, navigation patterns, entitlement changes, or profile updates.