Detect and Prevent Transaction Fraud Before it Impacts Your Business

Transaction fraud is any unauthorized or deceptive use of payment systems to complete purchases, redeem offers, or exploit financial processes. It can be carried out by both automated bots and human-driven schemes.

Common forms of transaction fraud include:

• Carding: Testing stolen credit card numbers through rapid, small-value transactions
• Scalping: Using bots to purchase high-demand items before legitimate customers can
• Inventory hoarding: Holding items in carts to create artificial scarcity without completing purchases
• Promo and coupon abuse: Exploiting promotional offers through fake accounts or automated workflows
• Loyalty point fraud: Stealing or manipulating reward points from compromised accounts

Without effective detection, these attacks lead to chargebacks, lost revenue, and damaged customer trust.

CHEQ detects transaction fraud through triple-layer intelligence that correlates Traffic, Trust, and Identity signals across the customer journey.

Traffic Intelligence identifies bots, spoofed devices, and behavioral anomalies. Trust Intelligence evaluates the integrity of the session and its data governance posture. Identity Intelligence assesses user data, cross-session behavioral consistency, and identity graph risk signals.

These layers work together to produce structured, explainable verdicts — each supported by reason codes and hierarchical evidence — enabling organizations to act with precision rather than relying on blunt blocking rules.

CHEQ is designed to help detect and mitigate a range of automated transaction threats. Key attack types include:

• Carding and credit card stuffing: Bots testing stolen payment credentials at scale
• Scalping bots: Automated purchasing of limited-availability products
• Inventory hoarding: Bots holding items in carts to create artificial scarcity
• Fake account creation: Generating accounts to exploit offers, promotions, or loyalty programs
• Gift card and loyalty fraud: Automated exploitation of stored-value and reward systems

CHEQ’s detection spans client-side and server-side signals, enabling coverage across web, mobile, and API environments.

CHEQ uses proportional enforcement to match the response to the risk level of each interaction. This means legitimate customers are not subjected to unnecessary friction.

Rather than applying blanket blocks, CHEQ enables organizations to configure a spectrum of responses: allow, monitor, step-up, constrain, throttle, misdirect, or block. The specific action is determined by configurable policies aligned to each organization’s business logic.

Detection happens behind the scenes through client-side and server-side challenges — over 2,000 per session — with no visible impact on page load or user experience. The result is security that works without compromising the checkout experience for real customers.

Transaction fraud affects any industry with online payment processing, but some sectors face disproportionate risk. The most commonly targeted industries include:

• E-commerce and retail: Carding, scalping, promo abuse, and inventory manipulation
• Financial services: Credit card stuffing, synthetic identity fraud, and account exploitation
• Travel and hospitality: Fraudulent bookings, loyalty theft, and inventory scalping
• Gaming and entertainment: In-app purchase fraud and virtual currency exploitation
• Online marketplaces: Fake seller accounts and payment manipulation schemes

CHEQ’s detection adapts to the specific fraud patterns and business logic of each industry through business logic calibration and policy management.

Traditional fraud detection typically relies on rules-based filters, CAPTCHAs, or single-layer signals like IP reputation or device fingerprinting. These methods miss sophisticated threats that spoof environments, mimic human behavior, or use synthetic identities.

CHEQ takes a fundamentally different approach through triple-layer intelligence — correlating Traffic, Trust, and Identity signals to assess the entity, its authenticity, and its intent behind each interaction. This enables entity-level trust decisions rather than binary block-or-allow outcomes.

Additionally, CHEQ provides explainable outputs — structured verdicts with supporting reason codes — so organizations can understand why a decision was made and use that data for downstream automation, fraud modeling, and compliance reporting.

Carding is a form of automated fraud where attackers use bots to test large volumes of stolen credit card numbers through rapid, low-value transactions. The goal is to identify which cards are still active before using them for larger fraudulent purchases.

CHEQ helps detect carding attacks through multiple correlated signals. Traffic Intelligence identifies automated tools, spoofed devices, and abnormal request patterns. Identity Intelligence flags disposable or inconsistent identity data associated with the transactions. Together, these signals help organizations identify and block carding attempts before they result in chargebacks.

CHEQ’s proportional enforcement allows organizations to apply the right level of response — from monitoring suspicious patterns to blocking confirmed attacks — based on their specific risk tolerance and business logic.

Identity Intelligence is the third layer of CHEQ’s triple-layer intelligence engine, focused on understanding who is behind each interaction.

For transaction fraud specifically, Identity Intelligence helps by:

• Detecting synthetic and AI-generated identities used to create fake accounts for fraudulent purchases
• Identifying compromised accounts through behavioral inconsistencies and new device or location signals
• Validating billing and shipping data against identity graph signals like address cycling and impossible travel
• Cross-referencing identity markers across sessions to detect patterns consistent with coordinated fraud

By combining Identity Intelligence with Traffic and Trust signals, CHEQ enables organizations to make more informed entity-level trust decisions at the point of transaction — helping catch fraud that single-layer detection methods miss.