Enhanced Signature Recognition and Fraud Detection with Deep Learning

Signature forgery undermines trust in financial, legal, and identity verification systems, costing industries billions annually. This review critically evaluates the efficacy of deep learning techniques—Convolutional Neural Networks (CNNs), Transfer Learning, and Hybrid Models—in detecting fraudulent signatures. By synthesizing findings from 40+ studies, we demonstrate that CNN-based methods achieve up to 92.3% accuracy by extracting spatial features like stroke curvature and pen pressure, while hybrid frameworks combining CNNs with attention mechanisms improve robustness against noise by 12%. However, challenges such as computational inefficiency, dataset bias, and poor interpretability hinder real-world adoption. We advocate for lightweight architectures (e.g., MobileNetV3) and explainable AI techniques to bridge this gap. This work not only maps the evolution of automated signature verification but also provides actionable insights for developing secure, scalable solutions tailored to dynamic fraud landscapes.