P337: Leveraging Generative and Conversational AI to Simplify and Interpret Complex Regulatory Guidance for Improved Compliance
Poster Presenter
Saurabh Das
Senior Consultant
Tata Consultancy Services Ltd United States
Objectives
The poster demonstrates how large language models can provide fast and useful insights for regulatory professionals. It showcases a case study on using these models to enhance regulatory intelligence.
Method
The study was conducted using a generative AI model trained on publicly available regulatory data. It involved testing a conversational AI assistant to process, summarize, and provide context-aware responses to compliance queries.
Results
The AI-driven system significantly improved the accessibility and comprehension of regulatory guidelines. Users were able to retrieve concise, accurate summaries of complex regulations in real time, reducing the time spent manually reviewing documents. The AI assistant provided dynamic, context-aware responses, enhancing engagement and decision-making.
Compared to traditional methods, AI-assisted regulatory analysis reduced compliance research time, improved interpretation accuracy, and minimized inconsistencies. Users reported increased confidence in their compliance decisions due to AI’s ability to extract key regulatory insights and clarify ambiguities.
The study demonstrated that AI models could effectively identify relevant regulatory provisions, align with compliance frameworks, and adapt to user queries. The tested hypothesis—that generative AI can enhance regulatory intelligence—was supported by improved efficiency and accuracy in interpreting regulations.
Conclusion
Generative and conversational AI have the potential to revolutionize regulatory intelligence by simplifying complex guidelines, improving information retrieval, and enhancing decision-making for compliance professionals. The study validates that AI-driven systems can bridge knowledge gaps, providing real-time, actionable insights that improve compliance efficiency.
Future implementations should focus on refining AI’s interpretability, incorporating real-time regulatory updates, and ensuring compliance with data privacy standards. Expanding AI-driven solutions to broader regulatory domains could further streamline compliance processes and reduce regulatory burden. By integrating AI into regulatory workflows, organizations can enhance agility, reduce manual effort, and achieve greater consistency in interpreting and applying regulations.
