Private equity firms work with vast amounts of data from internal and external sources. In this article, we’re going to talk about how an equity researcher can save valuable time by automatically generating equity research reports based on large data sets quickly and efficiently.

In our generative AI use case demo, we look at how you can streamline equity research with visual LangChain and SS&C Blue Prism. To learn more about generative AI, read our guide.

What Is LangChain In Generative AI?

LangChain uses large language models (LLMs) to simplify how a user can build applications. It lets software developers use artificial intelligence (AI) and machine learning (ML) along with other components to develop large language model applications.

Visual LangChain inserts LLMs into your business process workflows.

LangChain is the middleman between the user and the LLM. It’s useful for answering questions because it retains the context of a query regardless of how long or complex it is. This means you don’t have to repeatedly remind the LLM of the parameters or conversation history, resulting in a more seamless and natural interaction.

How is LangChain different from ChatGPT?

A LangChain framework links powerful LLMs like ChatGPT to external data sources to create natural language processing (NLP) applications that use knowledge of recent data, rather than being limited to a dated knowledge base. For example, when ChatGPT was released in 2022, it was built on the data available up until 2021, meaning the model’s knowledge and understanding were based on information up to that point and before. 

Applying AI To Equity Research

Now, let’s take this knowledge base and apply it as an automation solution in equity research.

What’s the challenge?

Most equity analysts still create research reports through inefficient manual processes. They log into multiple systems to extract data about the organization and its competitors, pull financial statements from one system, grab press releases and SEC filings from another, and then export Excel charts and graphs. It’s a repetitive process that’s time-consuming and prone to errors.

What should they do instead?

These analysts need a digital equity research assistant to augment their work by doing the initial legwork.

Can AI Do Equity Research?

It can, so let’s look at how it does this with a hypothetical scenario.

Ian the analyst implements a digital assistant

Ian emails his digital assistant, asking it to generate a draft equity report. When the assistant receives the request, it automatically pulls reports and exports charts. 

Watch generative ai and langchain equity research demo below: 

To create the draft report, the assistant synthesizes the data by evaluating the organization’s products, strategies and competitors and making financial projections.

Using visual LangChain , SS&C Blue Prism has built a no-code environment where Ian can describe the sections of the report and any critical questions he wants answered.

The assistant creates a first draft report using the outline section and answers Ian’s questions using the previously compiled research for context. Once the draft report is complete, the assistant emails the document and supporting research back to Ian so he can review it.

And voila! Ian can turn in the final report – saving time for himself and the business at large and also reducing the risk of errors with these carefully curated steps.

Watch the webinar to find out what happens when generative AI meets automation.

What If You Added Generative AI?

If we combined intelligent automation (IA) and generative AI, we could streamline the equity and investment research process with digital workers doing all the research by downloading reports and charts, and AI using that research to write the report.

AWS and SS&C Blue Prism are working together to develop secure and private intelligent automation (IA) that incorporates gen AI and other AI services at an enterprise level.

Want to learn more? Explore how you can turn your generative AI automation into a reality.

What Are The Direct Benefits?

Gen AI provides many benefits when used in concert with IA.

• Faster: Rather than taking up to ten weeks to perform equity research, generative AI could speed up that process to only a couple of minutes.
• Reduced errors: A digital assistant is accurate as well as efficient, ensuring fewer reworks and errors.
• Happier employees: Analysts are relieved of time-consuming repetitive tasks and can spend more time on engaging work, like forming conclusions and reviewing reports.
• Better accessibility: Your digital assistant, or digital worker, can read structured and unstructured data from multiple sources, on both public and private systems, helping your analyst access data more easily.
• Minimized risk: Your data is secure, which is especially critical in financial services and other highly regulated industries.

What Do You Need To Get Started?

If you’re interested in exploring how to prepare for generative AI, here are a few tips to get you started:

• Identify the risks.
• Evaluate use cases for generative AI in your automation plans.
• Ensure your AI training models adhere to good AI compliance.
• Establish AI governance by following best practices and setting rules.
• Prepare your infrastructure.
• Select the right AI tools for your business needs.
• Test and train your generative AI applications in a safe model development environment.

More banks, investment companies and financial institutions are utilizing data analytics and AI to assist analysts with sorting through huge data sets to discover hidden trends and patterns and derive valuable insights. As generative AI becomes more regulated, these organizations will look for more gen AI use cases in their automation, meaning analysts’ roles will transform to involve these technologies with digital assistants, making their work better.

Don’t wait. Think bigger with generative AI and intelligent automation.