Enhancing User Authentication for Financial Application Using Multi-Modal Behavioural Biometrics - A Survey

It is essential to ensure secure and easy user authentication in the domain of financial applications. Conventional techniques frequently fall short of offering strong security without sacrificing user pleasure. This research integrates fingerprint, signature, and eye movement patterns to provide a sophisticated multi-modal behavioral biometric identification system. In current systems, all three biometric modalities are infrequently integrated, especially for financial applications where security and usability are critical. The suggested solution promises to provide continuous authentication for mobile devices, improve authentication accuracy, and address issues such as template ageing. The multi-modal behavioral biometric is fused using Image-level fusion. It creates a single image map of fingerprint, signature, and eye movement patterns and matches it with the input image the system receives. In case of a perfect match the user is authenticated to use the service. A survey of 50 participants showed strong support for the multimodal approach, with 90% perceiving it as necessary and effective. By utilizing their combined capabilities and minimizing individual biometric weaknesses, the integrated system exhibits better security. This work also looks at the system's practical application and computing effectiveness on mobile devices with limited resources. This study offers a comprehensive solution for secure, effective, and user-friendly authentication in financial applications by filling in the gaps in the current research.