Privacy-Preserving Mortgage Data Validation Via Federated Learning: An Edge-AI Framework For Accelerated Application Processing

Abstract

The contemporary mortgage lending industry confronts unprecedented challenges in balancing operational efficiency with stringent privacy protection requirements that fundamentally threaten traditional application processing methodologies. This article presents a novel Edge-AI framework that leverages federated learning protocols to enhance mortgage data validation while maintaining strict privacy preservation standards throughout all processing stages. The proposed system utilizes on-device natural language processing and named entity recognition to locally extract and cross-validate critical data fields across multiple applicant documents without centralizing sensitive personally identifiable information. Advanced federated learning algorithms enable collaborative model training across distributed participants while ensuring individual financial data remains completely isolated within local device environments. The framework implements sophisticated privacy-preserving gradient sharing mechanisms that utilize homomorphic encryption and differential privacy techniques to prevent inference attacks on sensitive financial information. Experimental evaluation demonstrates substantial improvements in application processing efficiency and validation accuracy compared to traditional centralized systems while maintaining robust privacy protection against adversarial attacks. The Edge-AI framework achieves superior fraud detection capabilities through ensemble learning techniques that continuously adapt to emerging threat patterns without compromising applicant privacy rights. Integration capabilities with existing mortgage processing systems enable immediate industry adoption while providing enhanced regulatory compliance assurance across diverse jurisdictional requirements. The article establishes new paradigms for privacy-first financial technology implementations that prioritize consumer protection without sacrificing operational effectiveness or analytical sophistication in mortgage lending environments.