Moody’s has released Moody’s Research Assistant, a search and analytical tool powered by generative AI and using the company’s proprietary content and the latest large language models (LLMs) to help customers generate new insights from its credit research, data, and analytics.
The research assistant synthesises vast amounts of information allowing users to assess lending or investment opportunities, monitor developments, compare entities, and enhance analytical workflows rapidly and at scale. It also allows users to generate more holistic risk insights faster.
“Successfully navigating today’s complex risk landscape requires resource-intensive analysis of a vast array of research and data across a number of risk domains,” says Cristina Pieretti, general manager of digital insights at Moody’s Analytics. “With Moody’s Research Assistant, analysis that used to take hours can now be accomplished in minutes, freeing up more time for strategic decision making.”
Users that participated in a pilot of Moody’s Research Assistant reported gains in productivity and effectiveness. Based on observed metrics during the pilot, users could save up to 80% of the time they spend on data collection and up to 50% of the time they spend on analysis by adding Moody’s Research Assistant. Overall, results suggest the research assistant could save users up to 27% of their time spent performing the typical tasks and functions of a financial analyst.
Moody’s Research Assistant covers the latest rating actions, credit opinions, and research reports from Moody’s Investors Service to provide real-time answers for users. Ultimately, it will be expanded to leverage more of Moody’s data and content across risk domains including credit, climate, cyber, compliance, supply chain, and more.
The research assistant is available as an add-on to CreditView, Moody’s flagship ratings and research solution. Using Microsoft’s Azure OpenAI Service and powered by advanced language processing technology, Moody’s Research Assistant complements CreditView’s existing information retrieval system, effectively identifying relevant entities, industries, and geographical regions within textual content.
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