Building an Integrated Knowledge Graph for AI-driven M&A and Investment Due Diligence using Internal/External Data: An Automated Synergy and Risk Analysis Platform leveraging LLM and GNN

Currently, M&A and investment due diligence faces bottlenecks and the risk of overlooking critical insights due to the manual analysis of vast amounts of unstructured and structured data. This solution integrates LLM (Large Language Models) and GNN (Graph Neural Networks) to build a knowledge graph from internal and external data, automatically conducting in-depth analysis of synergies and risks, thereby revolutionizing the speed and accuracy of decision-making.

1. The Challenge / Context

The M&A and investment due diligence process is inherently fraught with immense information asymmetry and complexity. Data from various formats and sources—such as financial statements, legal documents, contracts, market reports, news articles, and social media—are scattered across different silos, making it nearly impossible for human cognitive abilities alone to grasp the subtle relationships and interactions between them. These limitations lead to the following serious problems:

• Time and Cost: Manual data collection and analysis require enormous time and human resources.
• Overlooking Risks: Potential risks hidden within complex connections (e.g., supply chain vulnerabilities, regulatory violation history, conflicts of interest) can be missed.
• Loss of Synergy Opportunities: Unseen synergy potential (e.g., technology integration possibilities, market expansion opportunities, key talent