Using Artificial Intelligence to Enhance Our Investment Processes

The launch of ChatGPT in November 2022 was a watershed moment. It unleashed a huge wave of interest in generative artificial intelligence (AI) and its possibilities. Leaders in virtually every industry across the globe are now evaluating how their businesses may be impacted by AI—and asset management is no exception.

While its popularity is relatively new, AI itself is not new to T. Rowe Price. For the past six years, we have been investing in capabilities around data science and machine learning to support our business and pursue positive outcomes for clients. Throughout this journey, we’ve been exploring how AI can be harnessed to connect our investment professionals to our firm’s wealth of knowledge, which is built on decades of fundamental research and learning.

To this end, our approach is one of “intelligent augmentation” (IA). Rather than automate decision‑making, we seek to empower our decision‑makers with additional data and insights, bringing new perspectives within the existing investment process. We believe this approach has the potential to transform the ways we work and enhance the outcomes we deliver for clients.

In addition to the benefits offered by generative AI, we believe our powerful, collaborative research approaches help to accelerate the learning process. It brings together senior leaders, portfolio managers, analysts, data scientists, software engineers, and user experience designers in a truly collaborative way. By supporting collective learning, it enables us to effectively navigate the rapidly changing landscape of AI technology.

A Model for Intelligent Augmentation

Recently, our Data Insights Group has focused on the potential of large language models (LLMs) to improve the delivery of data and insights to our portfolio managers and analysts. LLMs, of which ChatGPT is the most famous example, are computerized language models that are trained on vast amounts of text to generate human‑like responses to queries or prompts.

The ability of LLMs to instantly analyze vast amounts of data could prove invaluable. The sheer amount of information available on every potential investment we analyze is vast and continues to grow. Given the immense amounts of publicly available research and a deep archive of knowledge from our internal research platforms, technologies such as natural language processing (NLP) are becoming a necessity to help analysts retrieve and distill information.

To address this challenge, our Data Insights Group is developing a solution that would incorporate all the data and research we’ve amassed over many years to make that information significantly more accessible and retrievable by the appropriate investment adviser.

A solution that leverages an LLM and is tailored to the needs of our analysts and portfolio managers has multiple uses, which we classify as the three C’s: consumption, characterization, and creation.

Consumption: This involves how data and insights are retrieved for analysis. Consumption offers the biggest potential productivity gains in the near to medium term. An investment analyst might leverage an LLM to help learn more about a potential investment. The LLM facilitates this by rapidly analyzing and summarizing an aggregate set of information sources.

The analyst will then be able to conduct a back‑and‑forth conversation with the LLM to refine the request. This would enable an analyst to spend more time focused on evaluating the differentiating factors relating to individual companies that might make good long‑term investment prospects—through fundamental analysis, factor analysis, or insights from management interviews.

Characterization: This refers to the ability of AI to analyze unstructured data (such as text or images) to uncover complex but useful patterns that might otherwise be hard to identify. For example, academics in data science have analyzed years of the language used in 10-K reports. They’ve discovered a correlation between subtle changes in the presence of negative or positive words in those reports and subsequent stock returns. In a similar vein, we see huge potential in AI’s ability to review, in seconds, how sentiment on a stock has changed over time and to compare that with multiple data sources.

Creation: This refers to the way an LLM might also be used to draft content, including insights, investment updates, meeting notes, and other written materials. Automating aspects of content creation that were previously manual means that analysts can focus on more value‑added analysis and decision‑making.

Enhanced, Not Replaced, Human Decision‑Making

While AI‑powered tools have significant potential to automate tasks and magnify the insights of our portfolio managers and analysts, we are also cognizant of the potential risks and the need for people to monitor and manage them.

One key risk is bias. AI accesses vast amounts of information but cannot determine the reliability of that information. If the data used by an AI‑powered tool are biased, the algorithms created using that data will also be biased. Even the way a question is posed to an AI tool, known as a “prompt,” can introduce behavioral bias. For example, a negatively formulated prompt—such as “find holes in my thesis”—increases the risk of a negatively biased response, which may not be supported by the facts.

Another risk is around transparency. AI models can be complex and opaque, making it difficult to trace the basis of a response. This will clearly be a focus of regulatory scrutiny as capabilities evolve. We are also cognizant of privacy and security risks, as large volumes of data are consumed in training and using AI models.

Such risks warrant caution in the adoption of AI and the application of its outputs while our teams work to unlock its potential. Ultimately, we believe that investment processes augmented by AI will require human oversight and governance for successful active management.

Our preferred pathway is to harness AI to improve human decision‑making, create more efficient processes, and enable associates in key functions to focus on tasks that generate the most value. The journey we began six years ago, with a collaborative team of data scientists and investment associates, positions us to capitalize on the enormous potential of this rapidly evolving landscape.