The rapid rise of artificial intelligence has brought new opportunities and challenges to the world of regulatory compliance and risk management.
Shwetha Shantharam, AVP and product head at 4CRisk.ai, recently delved into what AI-powered regulatory intelligence products and solutions really need to do.
While generative AI has been widely discussed, concerns remain about its safety in enterprise settings, particularly when it comes to sensitive data. This is where the concept of combining Trustworthy Gen AI with specialised language models (SLMs) is becoming central to the future of regulatory intelligence.
AI-powered regulatory intelligence can deliver significant efficiencies and a competitive edge, but only if the technology is applied responsibly, Shantharam noted. The crucial step is to ensure solutions are built on foundations that respect privacy and are designed for compliance. Recognition within both the AI and RegTech communities is one sign of whether a product is genuinely advancing the field.
What distinguishes a trustworthy AI system from one that falls short is the level of transparency and rigour behind it. Secure systems follow zero-trust principles, support audit trails, and comply with global AI regulations. Equally important is explainability – trusted products should provide confidence scores, evidence linking, and use techniques such as LIME and SHAP to demonstrate how conclusions are reached. Transparency enables regulators and auditors to evaluate outputs with confidence.
Another critical area is governance. Strong regulatory AI products incorporate bias detection, fairness testing, and diverse data sourcing during model development. These systems undergo continuous validation to maintain accuracy and quality over time. A clear policy covering data acquisition, pre-processing, and tokenisation ensures that the models remain reliable.
Private, specialised language models play a crucial role. Unlike public large language models, SLMs are trained on curated regulatory, compliance, and risk-specific data. They are smaller, more efficient, and ensure that sensitive information remains within organisational boundaries. This approach not only avoids intellectual property concerns but also improves accuracy in highly specialised use cases.
Equally important is the application of advanced AI methods such as machine learning, natural language processing and retrieval-augmented generation. These tools power regulatory horizon scanning, rulebook curation, obligation management, and gap analysis. They also convert unstructured information into structured insights, mapping compliance taxonomies and highlighting relevant obligations across multiple jurisdictions and sources.
By combining trustworthy generative AI, specialised language models, and proven AI techniques, organisations can keep their RegTech systems future-ready, Shantharam said. Such an approach helps firms stay on top of regulatory changes, align compliance strategies, and ultimately gain an advantage in increasingly complex markets.
For more about AI, read the full story here.
Read the daily FinTech news
Copyright © 2025 FinTech Global
Copyright © 2018 RegTech Analyst
