Technological advancements have prompted the
emergence of peer-to-peer credit services which improve user
experience and offer significant reductions in costs. These advantages
may be offset by a higher credit risk, due to disintermediation
and information asymmetries. We postulate that networkbased
information can be employed as a tool for reducing risks
through an improved credit scoring model that increases the
accuracy of default predictions. Our research assumption is
proven by means of empirical analysis that shows how including
network parameters in classical scoring algorithms, such as
logistic regression and CART, does indeed improve predictive
accuracy.
