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

2026-03-24

AI Summary

This study addresses the challenge of identifying interlocking directorates in large Indian family-controlled firms—a phenomenon that poses significant conflicts of interest and compliance risks yet remains difficult to detect manually. To this end, the paper proposes the first scalable framework integrating graph theory, breadth-first search (BFS), and large language models (LLMs) to systematically analyze a network of over 50,000 directors across 85,000 companies. By combining structured network mining with semantic parsing of unstructured textual data, the approach effectively uncovers highly cohesive director clusters and elucidates their underlying formation mechanisms. The analysis reveals that 58.6% of directors serve on the boards of two or more companies, with 17% serving on exactly two, offering empirical support for enhanced corporate governance oversight and systemic risk assessment.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• interlocking directorships
• corporate governance
• director networks
• regulatory compliance
• family-controlled conglomerates

Innovation

• graph-theoretic framework
• Breadth-First Search (BFS)
• Large Language Models (LLMs)
• director interlocks
• corporate network analysis