Chargeback fraud occurs when legitimate cardholders intentionally dispute valid transactions to receive unauthorized refunds. Unlike traditional payment fraud involving stolen credentials, this attack vector exploits authorized user credentials to manipulate dispute-resolution systems.

Attackers complete purchases using their own payment information, receive goods or services as promised, then file false disputes claiming non-authorization or non-delivery. This exploitation of consumer protection mechanisms creates unique detection challenges because transactions appear completely legitimate during initial processing, requiring behavioral monitoring capabilities that extend beyond point-of-sale fraud detection.

Chargeback fraud manifests through three distinct attack patterns, each exploiting different vulnerabilities in payment dispute systems.

Friendly fraud is the most difficult to detect, as authorized account holders exploit their legitimate credentials. Customers dispute recurring charges, claim non-authorization, manipulate transaction recognition by claiming unfamiliarity with legitimate purchases, or contest completed services by asserting non-receipt.

These attacks leverage social engineering principles, in which fraudsters manipulate dispute-resolution processes by impersonating innocent consumers in complaints.

Criminal fraud involves unauthorized access to payment credentials, where attackers steal credit card information to execute fraudulent purchases. While more detectable through traditional cybersecurity controls, such as device fingerprinting and behavioral authentication, criminal fraud generates legitimate chargebacks when actual cardholders dispute unauthorized transactions resulting from account takeover.

Cyber shoplifting emerges as a sophisticated subset where customers intentionally receive goods or services and then systematically dispute charges, weaponizing the chargeback mechanism for theft. This attack pattern exploits information asymmetries in dispute-resolution processes, targeting digital services and accommodation providers where proof of delivery can be contested through phishing and credential theft, thereby obscuring transaction legitimacy.

Chargeback fraud systematically exploits vulnerabilities in payment dispute resolution systems through calculated deception across four distinct stages.

Fraudsters initiate legitimate transactions using valid payment credentials, ensuring purchases pass all initial security checks and appear normal to fraud detection systems. After receiving goods or services as promised, attackers create documentation trails that validate transactions while planning future dispute claims, exploiting trust relationships similar to vendor email compromise tactics.

The attack escalates when fraudsters contact financial institutions claiming non-authorization, non-recognition, or service delivery failures. Sophisticated actors manipulate evidence presentation, exploiting forensic gaps in authorization verification systems and limitations in transaction data visibility. This evidence manipulation mirrors invoice fraud techniques where attackers distort legitimate documentation to support false claims.

Traditional point-of-sale fraud detection systems fail against chargeback fraud because attacks occur entirely within legitimate transaction frameworks. Effective defense requires post-transaction behavioral analysis rather than authorization-time prevention, fundamentally shifting detection methodologies from transaction screening to pattern recognition across customer lifecycles.

Prevention requires layered security controls combining identity verification, behavioral monitoring, and automated response systems to address vulnerabilities across the payment lifecycle.

Key prevention measures include:

• Identity Verification: Implement comprehensive verification during customer onboarding to prevent fake accounts, similar to multi-factor authentication controls that verify legitimate users.

• Behavioral AI Monitoring: Deploy behavioral AI to identify suspicious transaction patterns and user behavior anomalies that traditional systems miss, leveraging techniques used to detect business email compromise attacks.

• Evidence Collection Systems: Establish robust documentation of transaction details, delivery confirmations, and customer communications to strengthen dispute resolution capabilities and protect against payment fraud and invoice manipulation.

• Multi-Layered Authentication: Integrate device fingerprinting, velocity checks, and behavioral authentication to verify legitimate users and prevent account compromise.

• Transaction Monitoring: Maintain comprehensive monitoring aligned with card network requirements to detect problematic patterns early, mirroring threat-detection approaches used in email security.

• Automated Response Mechanisms: Develop systems that reduce human error and accelerate incident response when suspicious activity occurs, leveraging AI-powered detection capabilities that adapt to evolving fraud patterns.

While chargeback fraud occurs at the transaction level, email-based attacks create vulnerabilities that trigger fraudulent payments and disputed transactions. Abnormal prevents invoice and payment fraud by stopping vendor email compromise and payment diversion at the email layer before fraudulent transactions occur.

The behavioral AI platform analyzes communication patterns to detect and block impersonation attacks, BEC scams, and phishing attempts that compromise payment processes. By identifying suspicious bank detail changes and bogus invoices before they reach finance teams, Abnormal eliminates fraud triggers that would otherwise generate chargebacks and dispute costs.

Ready to strengthen your chargeback fraud detection capabilities? Get a demo to see how Abnormal can help.