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Dan's new book for millennials, Wealthier: The Investing Field Guide for Millennials, will be published on May 15, 2024, and available on Amazon.
Our profession is being transformed by powerful, AI-based technologies that will replace human-based financial advice. They will drive down costs, reduce valuations, and deflate the multiples paid in M&A transactions.
You wake up to the reports of a new competitor. He looks like George Clooney, and he has the same engaging personality. He has the combined investing and financial planning knowledge of Charlie Ellis, Jack Bogle, Bill Bernstein, Burton Malkiel, Morgan Housel, and Larry Swedroe.
He’s available 24/7, 365 days a year.
He’s sensitive, self-aware, thoughtful, and empathetic.
If he can’t answer a question or the client wants more information, he immediately refers you to an hourly CFP® in his office.
The charge to access him is modest (assume $100 per month or less for unlimited access).
Would you be concerned about this competitor?
Current technology
This technology speaks from text and uses speech recognition, natural-language processing, chatbox frameworks, and avatar animation.
It’s currently available.
Here’s how I will be using it.
I already have an avatar, which I created on the HeyGen platform. I will use that platform to create a streaming avatar. I will use large language model (LLM) technology to upload the content of my new book. Readers can interact with my avatar through voice and text and ask questions about the book. They will receive an immediate response.
It’s not much of a leap to anticipate an enterprising firm licensing content from the prominent authors listed above (or others) and uploading their books to a streaming avatar.
What about financial planning?
Here’s how AI-generated financial planning would work.
Data collection
AI-powered software can collect client data through online forms, questionnaires, or direct access to financial accounts. This data may include income, expenses, assets, liabilities, risk tolerance, and financial goals.
Financial planning algorithms
AI algorithms can analyze the client's data to create a personalized financial plan. This plan may include recommendations for savings, investments, retirement planning, tax strategies, and more.
Monte Carlo analysis
AI can perform a Monte Carlo analysis to simulate various possible future outcomes of the client's financial plan.
Plan optimization
AI can continuously optimize the financial plan based on changing market conditions, life events, and client preferences.
Client communication
AI-powered tools can communicate the financial plan to the client in a clear and easy-to-understand manner, highlighting key recommendations and potential outcomes.
AI can make the financial planning process efficient, personalized, and data-driven.
What about the “human touch”?
Would you bet against MIT?
“Affective computing” focuses on developing systems and devices that can recognize, interpret, process, and simulate human emotions. The goal of affective computing is to enable machines to interact with humans in more natural and personalized ways by understanding and responding to human emotions.
Affective computing systems use sensors, machine learning, and artificial intelligence to analyze different aspects of human emotions, like facial expressions, voice tone, gestures, and physiological signals (like heart rate and skin conductance). By examining these signals, affective computing systems can infer a person's emotional state and adapt their responses or behavior accordingly.
MIT has been a pioneering institution in affective computing, playing a significant role in its development and advancement. Several researchers at MIT have made notable contributions to the field, particularly in emotion recognition, human-computer interaction, and the development of affective computing applications.
Platforms and systems demonstrate empathetic interaction between computers and humans, showcasing the capabilities of affective computing technologies. Here are a few examples:
Woebot
Woebot is a chatbot developed by Stanford University researchers that provides users with cognitive-behavioral therapy (CBT). It uses natural language processing and machine learning to simulate a therapeutic conversation, providing empathetic responses and emotional support to users dealing with mental health issues.
Read the full article here by Dan Solin, Advisor Perspectives
