Clustering Techniques for Discovering Patterns in Corporate Law Violations

Abstract

The increase in corporate law infractions such as fraud, insider trading, and colossal antitrust violations has made it imperative to have automated analytical tools that can derive structures and patterns within deep legal data such as compliance documents, case law, and regulations. A reliance on manual sifting through court documents, compliance documents and reports, and regulatory submissions is not only labor-intensive, but also fails to identify and capture the many intricately intertwined patterns of wrongdoing. This is the focus of the current study: to propose a clustering-based approach to identify recurring, emerging, and anomalous trends in datasets involving violations of corporate law. We draw from previous uses of clustering in monitoring human rights, analyzing antitrust law, and crime. Our approach incorporates the unsupervised methods of clustering: centroid-based (K-Means, K-Modes), hierarchical, density-based (DBSCAN), and fuzzy or probabilistic (LCA, GMM) in the legal field. Extracting features is done through TF-IDF and embedding representations with subsequent dimensionality reduction (PCA/LSA). Validation uses Silhouette, Dunn Index, Davies–Bouldin, and Calinski–Harabasz scores. Coherent clusters of violations identified through experimental clustering include fraud-corruption and data privacy-cybersecurity clusters, as well as anomalies of high regulatory concern. The results reveal the effectiveness of clustering and the use of legal analytics in compliance with regulatory frameworks and strategic policy. This study is a foundational effort in the application of unsupervised learning for the detection of corporate law violations, providing the growing domain of computational legal studies with a methodological framework and empirical validation.