The Great Wealth Transfer, Inequality and Preparing Heirs

The Great Wealth Transfer refers to a predicted transfer of wealth mainly from baby boomers (those born 1946-1964) to millennials and Gen Z households (HH). As younger generations are prepared to inherit this wealth we might expect significant changes in economic and social dynamics.

According to the 2022 Global Wealth Report published by Credit Suisse, global wealth has reached $436 trillion, with the United States being home to some 140,000 ultra high net worth individuals, (those with assets totaling over $50 million).

Though estimates vary as to the dollar amount of the great wealth transfer and its duration, as of 2023 the Federal Reserve Boards Survey of Consumer Finances and Financial Accounts reports baby boomers hold $77.1 trillion in assets, properties, businesses, and investments, a 52.8% share of $145.95T in total U.S. HH wealth.

Note: Amongst the group of 2,544 billionaires around the globe, 751 call America home and 1,000 are prepared to transfer some $5.2T to heirs over the next 20-30 years (UBS Billionaire Ambitions Report).

Wealth Inequality

The Federal Reserve Bank of St. Louis’ State of U.S. Wealth Inequality offers us these key takeaways:

The top 10% of households by wealth had $7.0 million on average. As a group, they held 69% of total household wealth.

Black families owned about 24 cents for every $1 of white family wealth, on average.

White households continue to own a disproportionately greater share of total family wealth. Though only representing 63.8% of households, white households owned 82% of total family wealth in the second quarter of 2023; this is 28% more wealth than their representation in the U.S. might predict. In contrast, Black families accounted for 14.2% of households and owned 4.5% of total family wealth (68% less wealth given their household share), while Hispanic families represented 10% of households and owned 3.1% of total family wealth (69% less wealth).

Successful Wealth Transfers

In the book Preparing Heirs: Five Steps to a Successful Transition of Family Wealth and Values, Vic Preisser and Roy Williams offer a checklist for successful wealth transitions. Their research of 3,250 wealthy families found that the most common reason for unsuccessful wealth transitions is a lack of communication between the wealth creator and their heirs. Notably, some 2 out of 3 wealth transfers do not go how the wealth creator intended.

Families might prepare by having conversations that will allow the heirs to gain a deep understanding of what the purpose of the assets are, including a stated mission. It may be prudent to define roles and responsibilities for each heir that extend beyond estate plan documents and have periodic meetings as individuals grow into their roles.

We imagine an extreme variation in financial and estate planning needs depending on race and culture. Most heirs decide to choose their own professional advisors. It should be noted technology has likely decreased the amount of assets required to operate family offices. Take time to reflect on where you and your family are on your journey and embrace the conversations that will protect your families assets long after your transition.

Related posts:

Do you need a will?

Four Concepts Wealthy Children May Know

Wealth: Net Worth and Liquid Net Worth

Income in the U.S.

Getting to the top…

Predictive Analytics and Artificial Intelligence in Financial Services

Predictive Analytics: the use of data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions (What is Predictive Analytics? – Harvard Busines School)

Generative AI: any AI system whose primary function is to generate content; This is in contrast to AI systems that perform other functions, such as classifying data (e.g., assigning labels to images), grouping data (e.g., identifying customer segments with similar purchasing behavior), or choosing actions (e.g., steering an autonomous vehicle). (CSET)

Machine Learning (ML): a field of computer science that uses algorithms to process large amounts of data and learn from it. Unlike traditional rules-based programming, ML models learn from input data to make predictions or identify meaningful patterns without being explicitly programmed to do so (AI in the Securities Industry, Financial Industry Regulatory Authority)

Of Cyborgs and Centaurs
Of Cyborgs and Centaurs

Deep Learning: a sub-field of machine learning that focuses on the development and application of algorithms inspired by the structure and functioning of the human brain, specifically artificial neural networks. It involves training neural networks with a large amount of labeled data to recognize complex patterns and make intelligent decisions or predictions.

Neural Networks: a set of interconnected processing units or nodes, designed to simulate the behavior of neurons in the human brain. Each node receives input data, processes it, and produces an output that is passed to the next layer of nodes. These networks can have multiple layers, enabling them to learn and extract high-level representations of data. By adjusting the weights and biases of the connections between nodes during training, neural networks can adapt and improve their performance.

Large Language Model (LLM): a type of AI system that works with language. In the same way that an aeronautical engineer might use software to model an airplane wing, a researcher creating an LLM aims to model language, i.e., to create a simplified—but useful—digital representation. The “large” part of the term describes the trend towards training language models with more parameters.1 A key finding of the past several years of language model research has been that using more data and computational power to train models with more parameters consistently results in better performance. (What Are Generative AI, Large Language Models, and Foundation Models? – Georgetown University Center for Security and Emerging Technology)

Financial Services Standards of Conduct

Earlier this year the Securities and Exchange Commission (SEC) proposed rules intended to protect investors from conflicts of interest arising from the use of predictive analytics and artificial intelligence by Broker/Dealers (B/D) and Registered Investment Advisers (RIA). The intent here is to prevent model bias where firms may put their interests before clients interests, put simply advisers and broker-dealers could use predictive analytics and AI to increase commissions. See Fact Sheet.

B/Ds and associated persons are held to a “best interest” standard of conduct. SEC Regulation Best Interest requires brokers to “Act in the best interest of the retail customer at the time the recommendation is made, without placing the financial or other interest of the broker-dealer ahead of the interests of the retail customer” (SEC Reg BI Final Rule).

Under federal law as a State of New Jersey RIA, Coroebus Wealth Management is a fiduciary. Consider fiduciary duty a standard of conduct comprising of a duty of care and a duty of loyalty (SEC Interpretation of the Investment Advisers Act of 1940). We do not act as a broker in transactions or earn commissions. We manage portfolios and charge a fee based upon the dollar amount of assets under management (AUM). B/Ds are incidentally excluded from the Advisers Act.

Suggested reading of cyborgs and centaurs: Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality – Harvard Business School

Coming Soon; Pythia is predictive analytics for entrepreneurs…