Tarun Ramadorai is a Professor of Financial Economics at Imperial College London. His research interests include asset pricing, household finance, international finance, economic policy, economic issues in emerging markets, and real estate. Professor Ramadorai works in both academic and policy roles. He holds fellow positions at the Asian Bureau of Finance and Economics Research, the National Council of Applied Economic Research, and the Center for Economic and Policy Research. Some of his former policy roles include being an Economic Advisor to the European Securities and Markets Authority, Chairman of the Inter-Regulatory Committee on Household Finance, and Visiting Scholar on the Economic Advisory Council to the Prime Minister of India. He is currently on the Norges Bank Investment Management Allocation Advisory Board.
Innovations in statistical technology, including in predicting creditworthiness, have sparked concerns about differential impacts across categories such as race. Theoretically, distributional consequences from better statistical technology can come from greater flexibility to uncover structural relationships, or from triangulation of otherwise excluded characteristics.Using data on US mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model,machine learning increases disparity in rates between and within groups; these changes are primarily attributable to greater flexibility.
