AI and Gen AI for Identifying Fraud for Credit and Debit Cards

Fraud is evolving as quickly as the payments landscape itself, and credit and debit cards remain a prime target. For banks and credit unions, staying ahead of sophisticated schemes requires more than traditional defenses, it demands the speed, scale, and precision of artificial intelligence.

Here is what we know about fraud in the environment.

• Fraud remains one of the largest cost centers for card issuers and banks
• Global card fraud losses are projected to exceed $43 billion annually by 2026 (Nilson Report)
• Fraudsters are leveraging AI-driven tactics (deepfakes, synthetic identities, and real-time social engineering)
• Traditional rule-based systems struggle against new, adaptive attack patterns

How can financial institutions leverage AI to identify and reduce fraud?

• Machine Learning & Predictive Models
  • Behavioral profiling: ML models monitor cardholder behavior across thousands of features (time of day, merchant category, location consistency)
  • Anomaly detection: Unsupervised ML flags unusual activity that rules-based systems miss
  • Adaptive scoring: AI assigns real-time fraud scores to transactions, reducing false positives
• Generative AI Applications
  • Synthetic fraud scenarios for training: GenAI generates simulated fraud cases, allowing models to learn from emerging attack vectors before they appear in the wild
  • Natural language analysis: Used to detect social engineering and fraud patterns in call center interactions, chatbots, and customer disputes
  • Identity verification: GenAI helps in document fraud detection (e.g., spotting manipulated ID images or deepfake videos)
• Operational Efficiency
  • False positive reduction: AI systems reduce “friction” for legitimate customers, cutting card declines
  • Case triage: AI prioritizes alerts for investigators, cutting review time
  • Fraud ring detection: Network graph AI models uncover linked accounts and mule activity

Financial Institutions that leverage AI for identifying fraud get the benefits through the following areas:

• Detection Accuracy
  • AI-based fraud systems improve detection rates by 20–30% compared to legacy systems (McKinsey)
  • Real-time ML models cut fraud losses by 15–25% for issuers
• Customer Experience
  • False declines represent nearly $331 billion in lost revenue annually (Aite-Novarica)
  • AI-driven scoring can reduce false positives by up to 50%, maintaining customer trust
• Operational Cost Savings
  • Banks report up to 40% reduction in manual fraud investigations with AI-powered triage
  • GenAI-assisted case notes & summaries reduce investigator handling time by 30–40%
• Industry Adoption
  • Over 70% of banks and card issuers have already deployed or are piloting AI-driven fraud prevention tools (PwC)
  • Payment networks (Visa, Mastercard) use deep learning on billions of transactions daily, blocking billions of dollars in fraud each year

When financial institutions utilize AI to identify fraud the strategic implications would be:

• Proactive Defense: GenAI enables issuers to anticipate fraud patterns instead of just reacting
• Balancing Risk & Experience: AI-driven scoring allows for precision in approvals, reducing friction while protecting accounts
• Scalability: With digital payments growing at double digits annually, AI offers scalable fraud prevention without proportional increases in headcount

What are the key takeaways for financial institutions?

AI and GenAI are transforming fraud management from rule-based detection to adaptive, predictive defense. They deliver measurable reductions in fraud losses, improve customer experience, and lower operational costs, making them a critical investment for any issuer or processor.

Contact us at Engage Fi for more information on how using AI technology can improve your overall institution performance.