Transparency in corporate networks: a graph-based model to reduce computational complexity in identifying total ownership of ultimate beneficial owners

Abstract

Identifying Ultimate Beneficial Owners (UBOs) in complex corporate structures is critical for financial transparency and preventing economic crimes. Recursive cycles in ownership networks exacerbate this challenge by increasing computational complexity. This article proposes a model based on weighted directed graphs, where nodes represent individuals or legal entities and edges represent ownership percentages. Integrating graph theory and geometric series efficiently resolves ownership cycles, providing a mathematical framework for calculating effective ownership. Direct ownership is computed as the product of weights along paths, while cycles are addressed using recursive algorithms and convergence factors derived from geometric series. The methodology combines graph modeling, algorithmic design (including a DFS version), and experimental validation. Preliminary results demonstrate that the model significantly reduces computational complexity (from O(n!) to O(n+m)), transforming intricate corporate networks into compact UBO tables with their total ownership. While its effectiveness depends on data quality, this work lays the foundation for scalable corporate transparency systems, with applications in financial regulation and compliance.