Nordea-fonden

Financial due diligence requires intensive analysis of vast unstructured documents (e.g., contracts, statements, invoices). However, traditional manual processing is inefficient, costly, and prone to subjectivity, and the existing automation solutions primarily focus on single-modal text recognition, lacking the capacity for joint understanding of multimodal features (e.g., layout, seals, table structures) and deep risk reasoning. This study proposes an end-to-end framework based on a Multimodal Large Language Model (MLLM) to bridge this gap. The framework not only performs accurate multimodal information extraction but also, integrates domain-specific knowledge (e.g regulatory clauses) to emulate expert-like reasoning. By constructing a dynamic risk knowledge graph that captures entities and relations across documents, it enables cross-document correlation analysis and anomaly detection. We will validate the framework on curated financial datasets, assessing both its information processing accuracy and risk diagnosis capability. Our contributions are threefold: 1) providing a novel computational linguistics solution that addresses the semantic and pragmatic challenges in financial document understanding; 2) advancing financial AI from perceptual to cognitive intelligence through explainable, knowledge-integrated reasoning; 3) offering a transparent, automated decision-support tool for high-stakes due diligence.