Websites’ data: a new asset for enhancing credit risk modeling

Recent literature shows an increasing interest in considering alternative sources of information for predicting Small and Medium Enterprises default. The usage of accounting indicators does not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing to credit. This complicates matters both for private lenders and for public institutions supporting policies. In this paper we propose corporate websites as an additional source of information, ready to be exploited in real-time. We also explore the joint use of online and offline data for enhancing correct prediction of default through a Kernel Discriminant Analysis, keeping the Logistic Regression and the Random Forests as benchmark. The obtained results shed light on the potentiality of these new data when accounting indicators lead to a wrong prediction.