AI-Driven Wealth Management Excludes $1M+ Clients: How Financial Exclusion Accelerates Under Tech Oligopolies
Original framing: “The ‘Mass Affluent’ Are Losing Their Allure for Wealth Managers Navigating AI” — Bloomberg
Scientific research on algorithmic bias in finance shows that AI systems trained on historical wealth data replicate and amplify existing inequalities, often excluding marginalised groups from financial services. Studies on robo-advisors (e.g., Fuster et al., 2022) demonstrate that these tools disproportionately favor high-net-worth individuals, reinforcing wealth concentration. The 'efficiency' narrative ignores these structural biases, framing exclusion as a neutral outcome.
The Bloomberg headline reflects a broader trend where financial oligopolies use AI to justify exclusion under the guise of 'efficiency,' obscuring how this deepens wealth inequality and erases non-Western wealth systems.
Historical patterns—from the 2008 crisis to the rise of neoliberalism—show that such exclusions are structural, not accidental, and disproportionately harm marginalised groups. Scientific evidence confirms that AI-driven tools inherit biases from training data, while Indigenous and communal wealth models offer alternatives that challenge the liquidity-centric paradigm. Regulatory intervention, community wealth funds, and alternative wealth metrics could mitigate these harms, but require dismantling the power structures that currently benefit from financial exclusion. The trickster’s irony lies in how 'progress' in wealth management is often a euphemism for further concentration, where the real inefficiency is not AI, but the greed it enables.