Enhancing Decision-Making in Mergers and Acquisitions with Graph Databases: Mapping Complex Networks

Authors

Satyam Chauhan

Abstract

Mergers and Acquisitions (M&A) are driven by complex networks of relationships between companies, investors, and stakeholders, making it challenging to track ownership structures, identify conflicts of interest, and ensure compliance. Traditional relational databases fall short when dealing with such interconnected data. This paper explores how graph databases, such as Neo4j, improve decision-making in M&A by providing superior capabilities for visualizing, analyzing, and querying complex networks. The paper features a comprehensive technical comparison between graph and relational databases, with performance benchmarks, use case studies, and future applications. Charts, tables, and graphs are provided to visualize performance gains and the practical applications of graph databases in M&A, highlighting their significant advantages in reducing processing time, improving compliance, and revealing hidden relationships.

Keywords

Graph Databases, Mergers and Acquisitions (M&A), Neo4j, Graph SAGE, Node2Vec, Predictive Analytics, Ownership Mapping, Conflict of Interest Detection, Corporate Hierarchies, Investor Influence, Machine Learning in M&A, Complex Networks, Relational Databases vs Graph Databases, Query Performance, Regulatory Compliance in M&A, AI and Graph Databases, Decision-Making in M&A, Entity Relationships

Enhancing Decision-Making in Mergers and Acquisitions with Graph Databases: Mapping Complex Networks. Satyam Chauhan. 2022. IJIRCT, Volume 8, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2411049