Who Sits Where? Automated Detection of Director Interlocks in Indian Companies
This provides an automated solution for regulators and governance analysts in India to monitor complex corporate relationships, though it is incremental in applying existing methods to a new domain.
The study tackled the problem of manually detecting interlocking directorships in Indian corporate networks by introducing a scalable graph-theoretic framework using BFS traversal and LLMs, analyzing over 50,000 directors and 85,000 companies to find that 58.6% of directors hold positions in two or more companies.
Interlocking directorships-where individuals simultaneously serve on the boards of multiple corporations-can facilitate the exchange of expertise and strategic alignment but also present risks, including conflicts of interest, economic 'oligarchy', and regulatory non-compliance. In contexts such as large, family-controlled corporate conglomerates in India, the manual detection of interlocks is hindered by the high volume of corporate entities and the complex involvement of extended familial networks. This study introduces a scalable, graph-theoretic framework for the systematic identification and analysis of interlocking directorships. Using Breadth-First Search (BFS) traversal, we examined a curated dataset comprising over 50,000 directors, 85,000 companies, and 300,000 director-company affiliations, yielding a comprehensive representation of corporate network structures. Large Language Models (LLMs) were integrated into the analytical pipeline to characterize both personal and professional linkages among directors. Empirical results indicate that 17% of directors hold positions in exactly two companies, while 58.6% maintain directorships in two or more companies. The combined BFS-LLM methodology enables the detection of recurrent director-company clusters, indicative of strong network cohesion, and provides qualitative insights into potential underlying drivers of these interlocks. The proposed approach enhances the capacity for automated, data-driven detection of complex intercorporate relationships, offering actionable implications for corporate governance, regulatory monitoring, and systemic risk assessment.