Network-Based Models to Improve Credit Scoring Accuracy

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.