Automated Detection of Fraudlent Signature’s Using Machine Learning

Signature verification is a critical process in banking, legal, and financial sectors to prevent fraud. Conventional manual verification techniques take a lot of time and are subject to human mistake. In this research, an automated machine learning (ML) method for identifying fake signatures is presented. To differentiate between real and fake signatures, we use feature extraction approaches, such as geometric, texture, and dynamic (where available) features. The classification accuracy of many machine learning techniques, including Random Forest, Convolutional Neural Networks, and Support Vector Machines (SVM), is assessed. The suggested method achieves high precision and recall rates by training and validating the model using datasets of both genuine and counterfeit signatures. When compared to traditional methods, experimental data show how well the ML-based strategy reduces false positives and negatives.