Who Sits Where? Automated Detection of Director Interlocks in Indian Companies

TL;DR

This paper presents a scalable graph-theoretic and AI-driven framework for automatically detecting director interlocks in Indian companies, addressing challenges posed by complex corporate networks and familial ties.

Contribution

It introduces a novel combination of BFS traversal and Large Language Models to systematically identify and analyze interlocking directorships in large, complex corporate datasets.

Findings

17% of directors serve on exactly two companies

58.6% of directors hold directorships in multiple companies

The method detects recurrent director-company clusters indicating network cohesion

Abstract

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.