Legal Analytics for Risk Management and Compliance

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ID: 318810
2026
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Abstract
Abstract As the regulatory landscape grows increasingly complex, legal analytics is emerging as a transformative tool for risk management and compliance. By applying advanced data analysis techniques such as machine learning and natural language processing to vast volumes of legal and regulatory data, firms can shift from static, retrospective approaches to dynamic, forward-looking strategies. Legal analytics enables organizations to identify trends, predict risks, and make informed decisions, fostering a more proactive and strategic legal management framework. This chapter explores the evolving role of legal analytics in business, highlighting its applications in privacy compliance, fraud prevention, and legal counsel monitoring. By contextualizing legal analytics within the broader discipline of Law & Management, this chapter underscores its potential to align compliance with competitiveness, redefine legal risk, and bridge the gap between legal and strategic decision-making in today’s data-driven business environment. Through case studies and empirical insights, the chapter outlines the evolution and the role of legal analytics, illustrating its applications across various domains such as data protection, fraud detection, and contract management. It highlights the growing use of AI-powered compliance tools, which assist firms in ensuring adherence to evolving legal standards while mitigating reputational and financial risks. Furthermore, legal analytics fosters a shift in corporate governance, positioning legal compliance as a competitive advantage rather than a constraint. Advocating for a new role, the legal analytics officer (LAO), the chapter argues that by bridging law, technology, and management, legal analytics redefines how firms approach compliance, legal risk, and regulatory oversight.
Reference Key
openalex_W7165948262 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors David Restrepo Amariles, Damien Charlotin
Journal Oxford University Press eBooks
Year 2026
DOI
10.1093/oxfordhb/9780197769034.013.0029
URL
https://doi.org/10.1093/oxfordhb/9780197769034.013.0029
Keywords Keywords not found

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