CLOSING THE WEALTH GAP: HOW ROBO-ADVISORS COULD REDUCE FINANCIAL INEQUALITY

Robo-advisors are automated investment platforms that use algorithms to provide financial advice and portfolio management at scale. They have gained prominence as low cost, accessible, and data driven alternatives to traditional human advisors,which often remain inaccessible to low income households due to high fees, minimum balance requirements, and incentive misalignment. This literature review synthesizes theoretical frameworks and empirical evidence to evaluate the effectiveness of robo-advising, with particular emphasis on its potential to improve financial outcomes for low-income individuals and families. Existing research shows that robo-advisors improve portfolio diversification, reduce volatility, and mitigate common behavioral biases such as the disposition effect and trend chasing. These effects are especially pronounced for novice and under-diversified investors, a group that disproportionately overlaps with lower-income populations. Despite these benefits, most robo-advisory platforms are not designed with low-income users in mind. Current models emphasize long-term investing over liquidity management, rely on surplus income assumptions, and offer limited personalization that fails to capture income volatility, debt burdens, or short-term financial goals. This review identifies these design and structural limitations and outlines future research directions focused on inclusive algorithm design,public or nonprofit deployment models,and regulatory frameworks that prioritize equity and consumer protection.