Abstract: Machine-Readable Code (MRC) and Machine-Readable Regulations (MRR) enable the conversion of complex regulations into structured formats such as JSON, XML, and X2RL, allowing machines to parse and interpret regulatory texts efficiently. Currently, organizations face challenges in regulatory compliance due to the complexity of regulations, frequent updates, and difficulty in identifying changes that impact policies and procedures. Existing literature provides guidance to a certain extent on how to anticipate regulatory modifications or ensure timely compliance. This review examines current literature on applying machine learning (ML) and Generative AI (GenAI) to extract, structure, and interpret regulatory content. It surveys techniques for converting regulations into machine-readable formats, predicting regulatory changes, and assessing alignment with real-world modifications issued by regulatory bodies. The findings indicate that using MRC, MRR, and AI enables automated compliance checks, faster detection of violations or errors, standardized compliance processes, real-time monitoring, and automatic report generation. These approaches can significantly enhance regulatory adherence across industries, particularly in sectors such as finance, where compliance is critical.

Samira Yeasmin and Bader Alshemaimri. “Bridging Machine-Readable Code of Regulations and its Application on Generative AI: A Survey”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.10 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161050

@article{Yeasmin2025,
title = {Bridging Machine-Readable Code of Regulations and its Application on Generative AI: A Survey},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161050},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161050},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {10},
author = {Samira Yeasmin and Bader Alshemaimri}
}