Retail fraud at Point of Sale (POS) checkouts, costing $30 billion annually worldwide, stems from weak identity verification, with 1–5% of transactions fraudulent and 60% of losses tied to high-value purchases (>$100). This study proposes an AI-driven system aiming to curb this by matching cardholder names with loyalty profiles in real time, supplemented by biometric (fingerprint) and behavioral biometric (gesture recognition) authentication for high-risk transactions. Leveraging natural language processing (92% accuracy) and anomaly detection (88% precision), the system flags 10% of transactions for secondary checks. In a simulation of 10,000 transactions (20% fraudulent), it detected 90% of frauds (1,800/2,000), including 450/500 high-value cases, far surpassing a baseline’s 30% detection rate (600). The system achieved low-risk transaction latency of 0.3 s and 0.6 s for high-risk cases with biometrics, compared to 1.5 s for traditional methods. It reduced false positives by 80% (500 to 100), chargebacks by 86% (1,400 to 200), and manual reviews by 80% (50 to 10 h/week). Implemented using POS fingerprint pads and gesture sensors, with Python-based simulations, this scalable, privacy-compliant solution enhances checkout efficiency for retailers like supermarkets and electronics chains. Biometrics outperformed behavioral methods in speed, though cost and adoption pose challenges. Future real-world tests will refine these findings, advancing AI and cybersecurity to bolster global retail resilience.