Crafting Trust with Convolutional Neural Networks and Hyperparameter Tuning for Precision Signature Verification in Insurance Claim

The prevention of signature forgery on physical documents is a critical concern in sectors like insurance, where the authenticity of claims documentation is pivotal. Handwritten signatures pose a challenge for verification systems due to inherent variability arising from diverse writing styles, pen types, paper qualities, and deliberate forgery attempts. To enhance the precision and resilience of signature verification in the context of insurance claims, researchers have explored various methods, with Convolutional Neural Networks (CNNs) emerging as a particularly promising approach. Therefore, this research paper aims to compare different CNN models in signature forgery detection, providing insights into their performance, strengths, and weaknesses, and contributing to the advancement of signature verification methods. Through this exploration, we aspire to lay the groundwork for the development of more sophisticated and accurate signature verification methods tailored to the unique challenges posed by insurance claim documentation. Ultimately, this research aims to fortify the security measures in place, ensuring the authenticity of insurance claims and mitigating the risks associated with fraudulent manipulations.