Information asymmetry is one of the fundamental problems that online peer-to-peer (P2P) lending platforms face. This problem becomes more acute when platforms are used for microfinance, where the targeted customers are mostly economically under-privileged people. Most of the prior empirical studies have been based on data from Prosper. com or similar sites that compete in traditional consumer loan markets. Our study examines P2P lending in microfinance for which borrowers are unbankable so that signals on creditworthiness of new borrowers are very limited. In addition, microfinance customers have more incentive to repeatedly seek loans from the market. Under this microfinance setting, we examine how lenders change their decisions as creditworthiness inference becomes increasingly possible through the accumulation of transaction history. Our findings confirm that lenders seek the wisdom of crowds when information on creditworthiness is extremely limited but switch to their own judgment when more signals are transmitted through the market. Different information sets are utilized according to the structures of decisions. Due to the possibility of a repeated game, it is also shown that borrowers try to maintain a good reputation, and direct communication with lenders may adjust incorrect inference from hard data when their creditworthiness is questioned. (C) 2012 Elsevier B.V. All rights reserved.