Financial Inclusion, Fairness and Stability in the AI Era (FinAI)

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Artificial intelligence (AI) has been rapidly adopted in the financial industry. It promises broad benefits for society, including more inclusive access to credit, better investment advice for households, and possible reductions in wealth inequality. Realizing these benefits is a priority for policy-makers, e.g., through the wide-ranging AI Act proposed by the European Union in 2021. However, there are urgent remaining challenges. First, while AI-driven finance can deliver low cost and high quality, its adoption is encumbered by a lack of trust and "algorithm aversion". Second, there are concerns about fairness and AI-based discrimination against disadvantaged groups. Finally, little is known about the implications of AI adoption for system-wide financial stability. Unfortunately, there is very little conceptual guidance on how financial institutions and regulators can address these challenges. My project is an ambitious agenda to advance our understanding of financial inclusion, fairness and stability in the AI era. I propose novel theoretical analysis and access to granular datasets from the financial industry, which will yield quantitative guidance to academics and policy-makers. The proposal is a unified block of research that breaks new ground on three levels: (1) AI Adoption by Individuals: Characterising the limits to the adoption of AI-driven financial advice, quantifying the dimensions of individuals' "algorithm aversion", and providing guidance for designing automated advice. (2) AI Adoption by Financial Intermediaries: Developing a flexible framework for "fair" deployment of AI-powered lending, including guidance for regulators, and optimal restrictions on algorithmic credit scoring. (3) Systemic Effects of AI Adoption: Providing the first rigorous analysis of financial stability in the age of AI, evaluating the consequences of AI-driven investments for systemic risk, and assessing the suitability of possible regulatory responses.