Banks and lenders are already using AI to decide who gets a loan, what interest rate they pay, and what investment advice they receive — but nobody has yet worked out how to stop these systems from discriminating against disadvantaged groups or destabilising the financial system. This project tackles three gaps in knowledge that regulators and financial institutions currently face. First, why do people distrust AI-driven financial advice even when it is cheaper and more accurate than human advice? Second, how should regulators define and enforce “fairness” in AI-powered lending without stifling innovation? Third, what happens to systemic financial stability when large numbers of investors and banks all use similar AI models at the same time — could they trigger a cascade of correlated decisions that amplifies a market crash? If successful, the research will produce quantitative guidance for policymakers implementing the EU’s AI Act and for financial institutions designing automated credit-scoring and investment tools. The project uses granular industry datasets and novel theoretical models to give regulators concrete rules for restricting algorithmic lending, and to show how AI-driven investments affect systemic risk. The work is applied and policy-facing: its direct output is actionable frameworks, not fundamental science.
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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.
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