Transforming Wealth Management: AI's Journey from Historical Invention to Financial Innovation

Have you ever considered how one piece of technology changed the course of history?

We’re not talking about the invention of the airplane or the dawn of the internet age. In fact it’s something you might use every-single-day.

The washing machine.

While many take it for granted, this simple, but amazing technological marvel of its time paved the way for new innovations that made you believe in magic. Beyond that, these early forms of AI brought forth massive social change, but also freed up hours and days of work for greater endeavors.

This fall, our co-founders, Patrick Reed and Gabe Rissman, gave two presentations where they discussed the history of AI technologies and how society has benefitted from AI enhancement.

In this blog post, we will share the history of AI technology, explain how it applies to Advisors and wealth management, and highlight a few ways Advisors can start integrating AI workflows into their practice.

Now time to get in the way back machine to the 1800’s.

The dawn of AI: A history of the technologies that felt like magic

The late 1800s witnessed a significant technological revolution that eventually paved the way for the development of AI. This era marked the emergence of transformative technologies such as electricity and automobiles, which dramatically transformed people's lives. However, among these, the humble washing machine played a pivotal role in catalyzing changes that were far-reaching and profound. By converting a day's chore into an hour's task, it not only redefined domestic life but also brought about significant societal shifts, particularly in gender roles and women's participation in the workforce. 

The washing machine's invention helped to relieve women most often of the burden of doing laundry by hand, a task that was both time-consuming and demanding. This new technology made it possible for women to have more free time, which they could use to pursue other interests and, in some cases, enter the workforce. The availability of more free time for women also meant that they could participate in civic and political activities, which was a significant milestone in the struggle for gender equality which was essential in the development and eventual passing of the 19th Amendment in 1920.

Keynes's Vision: The early dream of automation

Renowned economist John Maynard Keynes in the 1930's predicted a future where automation would significantly reduce labor requirements, leading to a life filled with leisure. Although his prediction of 15-hour workweeks hasn't materialized universally, it highlighted the potential of technology to reshape work and leisure and began the race for society to start pushing the limits of invention to reinvent the concept of how work can get done beyond labor.

ELIZA: The first conversational AI

In the 1960s, the first wave of AI, as we think of it now, was born with the development of ELIZA, a program created at MIT. The program was designed to simulate conversation, and while it was rudimentary in its approach, it caused a national sensation. ELIZA's popularity underscored the public's fascination and unease with AI's capabilities and its potential impact on society. Despite its simplicity, the program was able to engage in basic conversational exchanges with users, using pre-programmed responses to their inputs.

ELIZA's success also highlighted the initial overestimations of AI's potential, as many believed that such programs could soon replace human interactions. However, this period of experimentation and development was crucial in shaping public perception of AI and its perceived capabilities.‍

‍Machine Learning: The rise of statistical techniques

The 2010s witnessed a significant shift in the field of AI with the introduction of machine learning as a mainstream technique. Unlike earlier AI systems that relied on rigid logic, machine learning leverages statistical techniques to recognize patterns in large data sets. This has enabled AI systems to move from programmed responses to predictive capabilities, making them incredibly powerful and useful.

One example of this is BakeryScan, an innovative technology developed by the Japanese company Brain Co. Ltd. The primary objective of this tool is to recognize and differentiate between different types of bread and pastries in bakeries. Initially, it was used to simplify the checkout process in bakeries, where it could detect various bakery items without manual input. As a result, it reduced errors and accelerated transactions.

Interestingly, the technology behind BakeryScan found a novel application in the medical field, particularly in diagnosing cancer. Researchers recognized that the software's capability to analyze and differentiate intricate patterns in pastries could be repurposed to identify and categorize cancer cells in pathology slides. This transition from a bakery checkout aid to a medical diagnostic tool exemplifies an extraordinary case of cross-industry technology adaptation. The software's advanced pattern recognition algorithms, initially honed for baked goods, proved to be adept at analyzing complex cell structures and patterns, thereby aiding pathologists in diagnosing various types of cancers more efficiently and accurately. This unexpected development highlights the versatility and potential of AI-driven technologies in diverse fields.

The Era of Large Language Models: ChatGPT and other LLMs.

The current landscape of AI is dominated by large language models like ChatGPT. These models, based on algorithms developed over a decade ago, have gained prominence due to advancements in computing power. Unlike their predecessors, these models are generative, and capable of creating content and ideas, indicating a significant leap in AI's evolution.

Today's AI models are not only predictive but also interactive, capable of engaging in complex conversations and generating ideas. This development marks a significant step towards AI that can mimic human cognitive abilities, blurring the lines between human and artificial intelligence.

How AI is Empowering Financial Advisors

That changed at the end of 2022 with systems like ChatGPT demonstrating remarkable abilities the idea of AI creating magic saw an immense leap and adoption. These “large language models” can generate coherent essays, poems, programming code, and more upon request. Our team even used this new technology in the development of Advisor Core and other tools within YourStake. 

3 Opportunities for Financial Advisors to use AI in their tech stack now

1.Automating Manual Tasks

On average, financial advisors spend over 5 hours per week on manual duties like data entry. AI promises to eliminate these repetitive tasks through technology that can read, extract, and format this information in a format you can use.

For example, AI can scan a brokerage statement PDF and automatically log all the assets into portfolio management software. This lets advisors focus on higher-value activities like analysis and client meetings.

This technology is the first step in Advisor Core that can remove hours of work that are prone to human error. Learn more about how we built this technology to help you grow faster.

2. Drafting Marketing Content and Images

Creating quality content like newsletters, social media posts, and blog articles demands significant time. AI is capable of writing initial drafts tailored to an advisor’s brand voice that cover relevant industry topics.

Advisors can then review the AI’s work, making edits as needed and even have AI generating This approach saves hours of research and writing time with no prompt engineering needed to get a great first draft. 

Often an area most often outsourced or left on the back burner, Advisors can work on expanding their marketing capabilities to focus on retention and growth of their practice with little additional investment in resources or time. Just be sure to work with your compliance team to review all materials used for marketing or client use.

3.  Portfolio Analysis and Proposal Generation

Understanding a prospective client’s current investments is vital but often complex, depending on their unique holdings. AI helps instantly analyze portfolios, highlight risks, compare historical returns against benchmarks, identify costly overlaps, and more in mere minutes instead of days of work and re-reviews.

Advisors can have intuitive conversations with the AI, asking follow-up questions to fully grasp the portfolio’s strengths and weaknesses. This allows the creation of tailored proposals grounded in data, with talking points already generated using only data connected to the portfolio and no where else.

AI is instrumental al in this process to deliver valuable insights and analysis fast so you can close a prospect as soon as the second meeting. Allowing AI to help build these materials and run this analysis can help free up your teams capacity and capability to not only close more deals, but focus their work on higher level tasks and priorities.

See how this is done in the video below:

Looking Ahead: Is the future looking like Skynet or Keynes’ Vision? 

As we look to the future, AI continues to evolve, with research focusing on integrating various models to create systems that closely mimic human brain functions. While we might not know what the future bright bring, two roads often get brought up as the outcome, either we’re getting closer to the plot line of Terminator and Skynet will be coming online, or our future will match Keynes' vision, and we’ll all be on the beach as AI does most of our work.

We take a third approach and continue to think that no matter how AI develops, it will still be the washing machine of our work lives and we can focus on the human side of capital.