A Multi-Modal AI Framework for Detecting Document, Identity, and Income Fraud in Mortgage Lending

Mortgage fraud remains a persistent and evolving threat across the mortgage lending lifecycle, driven by increasingly sophisticated document forgery, synthetic identities, and income misrepresentation schemes. Traditional fraud detection systems often rely on siloed analytical approaches that evaluate documents, identity signals, or financial attributes independently, limiting their effectiveness against coordinated and multi-vector fraud attacks. Recent advances in artificial intelligence (AI) enable the integration of heterogeneous data modalities, creating new opportunities for holistic fraud detection frameworks.

This paper proposes a multi-modal AI framework for detecting document, identity, and income fraud in mortgage lending. The framework integrates computer vision, natural language processing, machine learning-based identity verification, and structured financial data analysis into a unified risk assessment pipeline. It emphasizes explainability, governance, and regulatory alignment to support adoption in highly regulated mortgage environments. Rather than introducing a new learning algorithm, this work contributes a reusable reference architecture that synthesizes existing AI techniques into a coherent, end-to-end fraud detection system. The proposed framework is intended to serve as a practical foundation for researchers and practitioners designing next-generation mortgage fraud detection platforms.