Strengthening fraud defenses with 28% better attack detection

The company needed to validate its fraud detection AI against advanced attack patterns developed by organized crime groups. The goal was to uncover vulnerabilities before fraudsters could exploit them, ensuring both security and seamless customer experience.

• Expose weaknesses in current detection thresholds
• Validate protection against synthetic identity fraud
• Assess system resilience to account takeovers and mule networks

Fraud techniques evolve constantly, outpacing static detection systems. While existing models could handle known patterns, they lacked resilience against novel, coordinated attacks.

• Fraudsters adapting quickly to bypass detection rules
• Synthetic identity fraud increasing in sophistication
• Account takeovers exploiting behavioral biometric gaps
• Money mule networks evolving to avoid monitoring
• Need to balance fraud detection with customer experience
• Previous testing relied on outdated fraud scenarios

CleverX engaged a panel of fraud and payments experts to red-team the system, simulating advanced and realistic fraud strategies.

Expert recruitment:

• Former financial crime investigators with deep knowledge of fraud rings
• Cybersecurity specialists understanding account compromise techniques
• Payment industry veterans familiar with transaction laundering methods
• Data scientists who previously built fraud detection systems

Adversarial testing approach:

• Creation of synthetic fraud patterns mimicking organized crime tactics
• Development of account takeover scenarios exploiting behavioral gaps
• Design of money laundering chains testing transaction monitoring
• Simulation of coordinated attacks across multiple account types

Quality assurance framework:

• Documentation of successful bypass techniques and their indicators
• Risk assessment for each identified vulnerability
• Cost-benefit analysis of potential remediation strategies
• Continuous testing against evolving fraud methodologies

The evaluation was delivered in accelerated phases to quickly expose vulnerabilities and strengthen defenses.

Days 1-3: Rapid expert onboarding and access to sanitized fraud data

Week 1-2: Analysis of current detection rules and historical fraud patterns

Weeks 3-6: Systematic adversarial testing with increasingly sophisticated attacks

Weeks 7-8: Implementation of detection improvements and retesting

The red teaming exercise revealed how legitimate-looking transactions could disguise fraud when attackers exploited thresholds and behavioral gaps.

Detection capabilities:

Experts helped the AI improve detection accuracy for sophisticated fraud scenarios.

• Better identification of synthetic identity patterns
• Improved recognition of account takeover precursors
• Enhanced detection of transaction laundering schemes
• More accurate flagging of mule account networks

Fraud prevention:

The system became more proactive at blocking advanced threats in real time.

• Reduced false negatives letting sophisticated fraud through
• Better real-time blocking of suspicious transactions
• Improved account security without impacting user experience
• Faster adaptation to new fraud patterns

Operational benefits:

The project reinforced resilience and positioned the firm as an industry leader.

• Reduced fraud losses while maintaining transaction approval rates
• Better prioritization of manual review queues
• Improved investigator efficiency with better alert quality
• Decreased customer friction for legitimate transactions

Strategic advantages:

• Proactive defense against emerging fraud trends
• Better intelligence on organized fraud operations
• Improved collaboration with law enforcement
• Stronger market position through superior fraud prevention

This project was recognized by a payments industry security consortium for advancing fraud prevention through adversarial testing.