In recent years, regulators and investors have been met with significant challenges due to the growing complexity of financial disclosures and fraud sophistication related to the market. Draft Red Herring Prospectuses (DRHPs), the vehicle for IPO regulatory review, typically contain large amounts of financial, structural, and narrative based information that is manual review is tedious and likely to contain contradictory information, similar information or misleading information. As a result, there has been more worldwide interest in using Artificial Intelligence (AI) to advance transparency and oversight of markets. This paper presents the IPO Foresight framework as an exploratory model to investigate the viability of using Natural Language Processing (NLP) and anomaly-detection techniques to analyze DRHPs when it is at the earliest stage. The purpose here is not to supplant human review, but to illustrate how AI-based insights, from semantic similarity verification to structural irregularities and summarizing relevant news in context, can support, supplement, or enhance the assessment processes in the traditional forms. The objective of establishing an interpretable and transparent automated system is to explore how AI can enhance the reliability, efficiency, and accountability of prospectus analysis. The work contributes to SEBI’s vision for responsible AI governance in finance and contributes to the ongoing conversation regarding the assistive capabilities of explainable machine learning to uphold financial disclosure integrity. The prototype achieved F1-score of 74.1%.