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

Prateek Sancheti, Kamalakar Karlapalem, Kavita Vemuri

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.

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

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.
Paper Structure (22 sections, 7 figures, 6 tables)

This paper contains 22 sections, 7 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Cliques, Maximal Cliques, and Maximum Cliques
  3. Frequent Itemsets and Maximal Frequent Itemsets
  4. Frequent Pattern Growth (FP-Growth) Algorithm
  5. Methodology
  6. Data Visualization
  7. Company-Company Graphs (Direct & Indirect)
  8. Director-Director Graph (Direct & Indirect)
  9. Maximal Cliques in Corporate Networks
  10. Maximal Frequent Itemsets in Corporate Networks
  11. Maximal Frequent Director Itemsets (MFDIs)
  12. Maximal Frequent Company Itemsets (MFCIs)
  13. Data Description
  14. Results
  15. Maximal Director Cliques (MDCs)
  16. ...and 7 more sections

Figures (7)

  • Figure 1: Company -- Director Graph: Blue Nodes (A--F) depict Companies, Orange Nodes (1--6) depict Directors.
  • Figure 2: Company -- Company Graphs (Direct and Indirect). (a) Direct Company -- Company Graph (b) Indirect Company -- Company Graph.
  • Figure 3: Director -- Director Graphs (Direct and Indirect). (a) Direct Director -- Director Graph (b) Indirect Director -- Director Graph.
  • Figure 4: (a) Maximal Company Cliques for Figure \ref{['fig:cc-graph']} (a) - 3-Vertex Clique (ABC) (b) Maximal Director Cliques for Figure \ref{['fig:dd-graph']}(a) - 5-Vertex Clique (12345)
  • Figure 5: (a) Frequency of Number of Directors Corresponding to a Company (b) Frequency of Number of Companies Corresponding to a Director.
  • ...and 2 more figures