The framework itself is straightforward. Once a question or issue is identified, decision-makers gather a swath of data to gain a comprehensive view. After synthesizing that information and extracting insights, they reach a decision and take the appropriate actions that follow from that decision. Monitoring outcomes ensures that learnings are applied to refine the process continually.
Whether decisions are investment-related or pertain to business operations, most, if not all, of them leverage this framework. That makes it a useful way to assess the effectiveness of developments in a rapidly evolving field, including a proliferation of data sources and tools.
Evaluating a New Wave of Data Sets and Tools
Over the past decade, the investment-management industry has seen a huge influx of new data sources and more sophisticated analytical tools. The tools include generative AI, which is designed to create new content or data—not just analyze or process existing data.
These newcomers have enabled data scientists to sharpen their game. Timely data such as foot traffic and online inventories, for instance, can help investors proxy company revenues, enabling them to assess fundamentals faster than they used to. And powerful generative AI capabilities can be leveraged to generate code and text across a variety of business processes.
While machine learning is very effective at extracting insights during the decision-making process, generative AI is a much more robust tool. It plays a sizable role in three stages of decision-making: creating synthetic data, extracting insights from data and recommending actions.
Using the framework as a lens, we can assess the capabilities and impact of many tools and techniques, helping investors determine which ones are most relevant.
Getting More Systematic with Decision-Making
Increasingly, investment teams are striving to create repeatable processes to identify opportunities, and both discretionary and systematic managers are using screening tools to streamline processes. For many investors, though, the tools do just that—screen. Human analysts must kick the tires before deploying investment ideas in portfolios, so the “take action” stage of decision-making is still largely people-driven.
Systematic investors are changing this paradigm, creating algorithms that seek to make automated decisions based on inputs. Generative AI pushes the envelope even further, recommending actions or steps across diverse processes. In customer service, for instance, chatbots can handle routine, repetitive and time-consuming tasks. The result? More free time for humans to tackle the complex issues.
Systematizing decision-making is not only efficient, it’s necessary. More data sources, better algorithms and timely responses are demanded in investment decision-making, client servicing and operational processes. To harness this trend, the industry can leverage generative AI, combined with natural language processing (NLP), to make better decisions.
The Alphabet of Low-Hanging Fruit: NLP, NLU, NLG
NLP, as well as natural language understanding (NLU) and generation (NLG), enable models to read text inputs, make sense of them and create responses. Chatbot applications are the most obvious uses, but the applications for investment management are wide-ranging.
NLP excels at handling essential tasks that unlock the potential of text-based information (Display), enabling asset managers to gauge market sentiment, discern trends, identify sales and investment opportunities, extract key data and insights from long documents, and create solutions and responses to issues or questions.
