The Application of Retrieval-Augmented Generation (RAG) in Developing an Intelligent Risk Management Platform: A Case Study at Statistics Jawa Timur

Abstract

Risk management is a crucial element in the governance of modern organizations, especially for public institutions such as Statistics Indonesia (BPS), which is responsible for providing official state statistics. Currently, the conventional methodology at Statistics Jawa Timur remains manual, relying on spreadsheet software, which results in slow and unresponsive processes for addressing dynamic risks. This condition reduces the effectiveness of internal controls, particularly with a massive strategic agenda like the 2026 Economic Census (SE2026) approaching. To address these limitations, this research proposes the development of Kadiri-A Risk Management Information System and Worksheet, an intelligent system that integrates Artificial Intelligence (AI) technology, specifically Large Language Models using the RetrievalAugmented Generation (RAG) method. The Kadiri system is designed to transform risk management from a reactive to an initiative-taking process, accelerating the identification, analysis, and mitigation recommendations by leveraging BPS internal knowledge base. The RAG methodology enables an AI model, such as Google Gemini, to provide contextual and relevant suggestions based on the organization's historical data. The outcome of this development is a digital platform that speeds up risk analysis, enhances accountability, and aligns with the bureaucracy reform agenda.