Ahead of the Regulators: AI-Powered Risk Flagging in Corporate Governance Frameworks

Parveen Shukla
DOI: http://dx.doi.org/10.19085/sijbpg120301

Abstract

Corporate governance frameworks across the world are increasingly tested by the complexity of multi-regulator environments. A single large corporation may simultaneously answer to a central banking authority, a securities markets regulator, an insurance oversight body, a sector-specific watchdog, and several others, each with its own reporting standards, compliance timelines, and penalty structures. Staying ahead of this web of obligations demands more than periodic audits or compliance checklists. This paper examines how specific artificial intelligence (AI) technologies, including machine learning, natural language processing (NLP), and predictive analytics, can be built into corporate governance structures to flag default risks before they reach the attention of regulators or other stakeholders. Drawing on established theories of corporate governance and existing scholarship on AI applications in financial compliance, the paper argues that proactive AI-assisted risk flagging is not merely a technological upgrade but a structural shift in how boards and management conceptualize their accountability obligations. The paper also draws on the Indian regulatory context, with bodies such as the Reserve Bank of India (RBI), the Securities and Exchange Board of India (SEBI), the Insurance Regulatory and Development Authority of India (IRDAI), and the Real Estate Regulatory Authority (RERA), to illustrate the practical demand for such systems in emerging market governance frameworks.

Keywords

Corporate governance, artificial intelligence, compliance risk, RegTech, predictive analytics, natural language processing, multi-regulator environment, risk flagging

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