Study Overview
Algorithmic lending has led to a rise in personalized pricing reflecting information beyond the credit score. We study the welfare effects of pricing on increasingly granular information. We use a randomized pricing experiment and causal machine learning to estimate demand and cost curves and embed them in competitive equilibrium. Finer pricing need not improve welfare: relative to uniform pricing, credit-score pricing reduces total surplus, as prices increase in adversely selected low-score pools, pushing out safest borrowers.
Study Results
Personalization improves on the score by separating observables predicting risk levels from those predicting selection, but uniform pricing dominates, as lending gains remain modest.
Intervention: Randomized variations in interest rates for consumer loans
Populations: Middle income households
IBSI Initiative or Center: Lab for Inclusive FinTech (LIFT)