Generative AI for financial document summarization and risk analysis

Batbaatar, Narangarav (2025) Generative AI for financial document summarization and risk analysis. World Journal of Advanced Research and Reviews, 26 (3). pp. 1925-1937. ISSN 2581-9615

This paper discusses how generative AI can be applied to the field of financial document summarization and risk analysis to handle the issues of high volumes of complicated financial information. The main goal consists of determining how effective AI-based models can be when it comes to summarizing financial documents and improving the process of risk assessment. The study provides a mixed-methods investigation of the case studies of AI-powered systems implemented in financial institutions, as well as a performance analysis according to the major metrics, including accuracy, efficiency, and risk prediction. Among the main insights, it is possible to mention that generative AI considerably enhances the quality and speed of summarization of financial documents, allowing institutions to analyze huge volumes of data in real-time and improving risk analysis. Additionally, machine learning models have a competitive advantage in eliminating people error and biasness in risk assessment. This study is important as it explains how generative AI has the potential to transform financial document processing and risk management to provide viable solutions to financial institutions to enhance the decision-making process, minimize operational expenses, and broaden the scopes of overall risk management. The paper has ended with suggestions on the future AI use in finance.