Morgan Stanley Revolutionizes Knowledge Search with GPT-4, Accelerating Advisor Operations

Which company is this case about?

Morgan Stanley is one of the world's leading financial services firms, engaged in investment banking, securities, and wealth management. Particularly in its wealth management division, financial advisors with specialized knowledge provide services to clients, and maintaining and improving the quality of their work is constantly required.

Challenges that needed to be solved

To provide optimal recommendations to clients, the company's advisors needed to refer to a vast and complex internal knowledge base, including over 70,000 market research reports and investment strategy documents issued annually. With conventional methods, finding the necessary information from this enormous volume of data took a lot of time, contributing to a decrease in advisor productivity.

How AI was used

Morgan Stanley partnered with OpenAI to develop an internal AI assistant called 'AskResearchGPT' which leverages the GPT-4 model specifically tailored to its extensive internal knowledge base. When an advisor inputs a question in natural language, this system searches and extracts relevant information from over 100,000 internal documents, summarizes their content, and generates comprehensive answers. This is believed to utilize a technology called Retrieval-Augmented Generation (RAG), enabling highly reliable answers based on the company's closed internal data.

Implementation Effects and Key Takeaways

• ▸The time spent on information retrieval was dramatically reduced, freeing up advisors to focus on client interactions and more strategic tasks.
• ▸Advisors gained instant access to necessary information, enabling them to provide deeper, more personalized, and higher-quality services to clients.
• ▸The key point of this case is that by utilizing highly specialized internal information (knowledge) accumulated by the company itself, rather than general web information, AI creates business-specific value while also ensuring information security.
• ▸The implementation of a rigorous evaluation framework and mechanisms to ensure the accuracy of answers (such as displaying source documents) was also a crucial factor for its success in the highly regulated financial industry.

What Japanese companies can learn

This case study is applicable not only to the financial industry but also to any industry with a large volume of specialized knowledge and internal manuals, such as legal, medical, and manufacturing. Enabling everyone to utilize the 'knowledge assets' accumulated within the company through AI directly leads to improved employee productivity and service quality. An effective approach would be to initially introduce it experimentally as an FAQ response system for a specific department or an internal policy search system, and then expand it while measuring its effects.

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