Edge-Deployed AI for Intelligent Financial Document Processing and Fraud Detection: A Technical Review

Abstract

The proliferation of mobile banking and financial service applications has revolutionized document processing workflows with remote capture and validation capabilities. Traditional server-based systems for processing financial documents including checks, mortgage instruments, trust agreements, and tax compliance forms capture images and forward them to centralized computing infrastructure for optical character recognition and validation workflows, which expose weaknesses in terms of latency, privacy risks, and operational expenses. Contemporary edge computing paradigms allow artificial intelligence models to run directly on mobile devices, freeing up backend servers and processing sensitive financial documents in local settings. Resource-efficient anchor-free object detection networks designed for use in constrained environments enable real-time extraction of critical document components essential for validation and authentication workflows across diverse financial instrument types. Architectures deployed at the edge exhibit substantial benefits in the form of lower transaction latency, better privacy safeguarding with localized processing, lower infrastructure expenses, and increased reliability under connectivity-limited circumstances. Federated learning mechanisms facilitate ongoing model improvement without centralized sensitive data, maintaining user privacy while enhancing detection capabilities. Persistent challenges include model drift due to changing document designs, adversarial attack susceptibility, device security needs, and governance complexity for distributed deployment. Hardware-software co-design efforts hold out the promise of specialized neural processing units with custom operations supporting document intelligence tasks, allowing for more advanced capabilities within mobile form factors and power budgets.