André Sztutman from the Tepper School of Business at Carnegie Mellon University will present "Optimal Credit Scores Under Adverse Selection"

Abstract: The increasing availability of data in lending markets brings hope that information asymmetries may disappear and financial inclusion will improve. However, if the limited access to financial services is due to adverse selection, more information will not necessarily increase lending volume or borrower surplus. We provide tools for better using the data on potential borrowers, formulating and solving the optimal disclosure problem of an intermediary who maximizes surplus. We show that any optimal disclosure policy needs to satisfy a simple set of conditions that relate prices, markups, price elasticities of profits, and price elasticities of the expected value of the loans for the investors. Empirically, we apply our method to the data from the Townsend Thai Project -- a long panel dataset with rich information on credit histories, balance sheets, and income statements -- to evaluate whether it can help develop the particularly thin formal rural credit markets in Thailand and we find economically meaningful gains from adopting limited information disclosure policies.