NEURAL NETWORK-BASED SIGNATURE PROFILING FOR FRAUDULENT AUTHORIZATION DETECTION

Abstract

This paper presents a neural network–based signature profiling framework for detecting fraudulent authorization attempts in both digital and physical authentication environments. By integrating artificial intelligence (AI), advanced cybersecurity protocols, and state-of-the-art signature verification techniques, the proposed system learns unique behavioral and biometric characteristics from authorized users and identifies anomalies indicative of potential fraud. Leveraging datasets up to 2024, including real-world e-banking transactions and high-resolution signature image corpora, the model achieves high detection accuracy with minimal false positive rates. Experimental results demonstrate significant performance improvements over conventional rule-based and statistical anomaly detection approaches, highlighting the framework’s scalability, adaptability, and applicability to modern cybersecurity infrastructures.