How Morgan Stanley Uses Large Language Models (LLMs) in Finance?

Morgan Stanley is leading the way in using advanced AI technology, especially Large Language Models (LLMs), to improve its services and streamline operations.

As a global investment bank, they’re using LLMs to make smarter decisions, connect better with clients, and speed up internal processes. Here’s how they’re using this tech to stay ahead in the finance world.

Making Financial Research Smarter

At Morgan Stanley, LLMs play a key role in financial research. These AI models can analyze large amounts of data like market reports and financial news, then summarize the most important information.

This helps analysts and investors make quicker, more informed decisions. A Forbes report states that using AI in finance can reduce the time spent on research and data analysis by up to 30%.

Helping Wealth Managers

Morgan Stanley’s wealth management team also benefits from LLMs. The AI tools help advisors review client portfolios, suggest investment strategies, and assess risks.

This enables advisors to give personalized advice that matches each client's financial goals. Clients get more relevant recommendations, strengthening their relationship with the bank.

Improving Customer Service

LLMs are also used in chatbots and virtual assistants at Morgan Stanley. These AI tools quickly answer client questions about account details, market trends, and investments. By automating these routine tasks, clients get faster, more consistent responses.

According to a Gartner report, by 2025, 70% of customer service interactions in all industries will be powered by AI, showing the growing importance of AI in customer service.

Staying Compliant with Regulations

LLMs also help Morgan Stanley stay on top of legal and regulatory changes. The AI models analyze important documents to ensure the bank follows financial regulations correctly, reducing the risk of mistakes.

In conclusion, Morgan Stanley’s use of LLMs is changing the way they work, communicate with clients, and analyze markets.

By adopting this technology, they’re not just improving efficiency—they’re also offering more personalized, valuable services, setting a new standard in the finance industry.