Publication Details
Abstract
The growing complexity of regulatory compliance in the BFSI sector has highlighted the need for transparent, auditable, and AI-driven decision-making platforms. Traditional compliance workflows are often fragmented across multiple systems, resulting in inefficiencies, inconsistent enforcement, and challenges in regulatory reporting. This article proposes the design of a centralized AI compliance platform that integrates LangChain for intelligent agent orchestration, Amazon Bedrock for scalable foundation model deployment, and knowledge graphs for structured reasoning and traceability.
The platform enables end-to-end compliance automation by unifying disparate data sources, applying advanced natural language processing to regulatory text, and generating auditable compliance decisions in real time. Knowledge graphs ensure semantic understanding, lineage tracking, and explainability, while LangChain orchestrates AI reasoning across modular components, and Bedrock provides scalable, secure access to foundation models for predictive and prescriptive analytics.
Through illustrative use cases in risk assessment, regulatory reporting, and internal policy enforcement, the article demonstrates how the platform enhances operational efficiency, reduces regulatory risk, and strengthens transparency in decision-making. The study emphasizes the importance of AI governance, auditability, and human-in-the-loop oversight in BFSI compliance, offering a practical blueprint for integrating emerging AI technologies into enterprise regulatory frameworks.
The findings underscore that a centralized, AI-augmented compliance architecture not only accelerates regulatory processes but also establishes trust, traceability, and resilience, positioning BFSI organizations to meet both current and evolving regulatory demands.