Sovereign Risk Summarization

Abstract

This paper introduces an LLM-based approach for analyzing and summarizing factors relevant to sovereign risk ratings for foreign countries. Our approach automatically generates sovereign risk reports by summarizing the natural language source documents, and is the first approach that generates risk reports that are qualitative in nature. It utilizes GPT-4 to automatically interpret and extract key summary points from extensive data sets, and compiles them into a comprehensive report. Our approach preserves temporality in summarizations, assigns citations to the generated summaries, and checks for hallucinations. Our solution, which could be used for reports from approximately 120 countries, demonstrated a significant reduction in analysis and report generation time from one to two weeks (manual approach) to just one to two hours, while maintaining an F1 score exceeding 0.7, indicating very good accuracy. These results suggest that our approach could greatly improve the sovereign risk rating process, enhancing efficiency while ensuring comprehensive information coverage and easy validation.