The increasing sophistication of financial fraud necessitates intelligent, real-time security frameworks capable of identifying and mitigating threats before they escalate. This research presents an AI-driven security system that integrates facial recognition and behavioral profiling to detect fraudulent activities within financial environments. The core objective is to enhance traditional security measures by introducing a dual-layered verification approach, combining biometric authentication with behavioral anomaly detection. The system architecture is built around convolutional neural networks (CNNs) for facial recognition and recurrent neural networks (RNNs) for real-time behavioral analysis. These models operate collaboratively, enabling continuous authentication and fraud risk assessment based on user identity and interaction patterns. Experimental evaluations were conducted using a hybrid dataset comprising facial imagery and synthetic behavioral logs representative of banking operations. Quantitative results show that the facial recognition module achieved an accuracy of 96.3% with an F1-score of 95.9%, while the behavioral profiling module attained an accuracy of 92.7% and an F1-score of 92.3%. The integrated decision system further improved overall performance, reaching an accuracy of 94.8% and an F1-score of 94.3%, demonstrating the effectiveness of multimodal fusion. The proposed system demonstrated high accuracy in recognizing authorized users and detecting deviations associated with fraudulent intent, achieving an F1-score exceeding 92%. Performance benchmarking indicates that the facial recognition, behavioral profiling, and fusion components incurred latencies of 85 ms, 140 ms, and 60 ms respectively, resulting in an end-to-end system latency of approximately 285 ms with a throughput of 6–10 sessions per second. Moreover, latency benchmarks confirmed its suitability for real-time deployment without significant processing overhead. The findings highlight the viability of merging facial biometrics with behavioral analytics to build proactive, adaptive security mechanisms. This study contributes to the development of next-generation fraud detection tools by emphasizing real-time responsiveness, layered intelligence, and contextual awareness. The proposed framework has strong potential for deployment in banking, fintech applications, and secure transaction platforms where identity integrity and behavioral trust are critical.

Keywords: AI-Driven Security, Facial Recognition, Behavioral Profiling, Real-Time Fraud Detection, Financial Security, Biometric Authentication