Accelerate Regulatory Compliance with AI-Powered Precision
As open banking regulations evolve, compliance leaders face the complex challenge of interpreting and implementing dense financial laws like Section 1033 of the Dodd-Frank Act. With deadlines extending through 2030, ensuring accurate, consistent understanding across legal, risk, and customer-facing teams is more critical than ever.
Our latest whitepaper explores how fine-tuning the Granite model using IBM’s InstructLab methodology on watsonx.ai transforms compliance workflows. By leveraging synthetic data AI, LTIMindtree and IBM built a highly specialized model that converts regulatory text into plain-language, actionable insights for banks and financial institutions.
Through a scalable, repeatable process, the fine-tuned model delivers precise, domain-specific responses—reducing manual effort, minimizing misinterpretations, and accelerating decision-making. From taxonomy-driven data curation to multi-phase instruction tuning, the methodology ensures accuracy and efficiency while enhancing retrieval-augmented generation (RAG) performance.
