Enhancing SMEs default prediction with web-scraped data

The objective of a Credit Risk model is to develop an accurate rule that can distinguish between good and bad instances. In this work instances are firms, specifically we focus on Small and Medium Enterprises (SMEs), as most of the papers in the financial literature, thanks to their contribution to the European Union economy for both value added and the creation of jobs. The aim of the paper is twofold: to explore the usage of the web-scraped indicators to improve firms’ default predictions and to compare the models’ performances fed with the web-based indicators with the ones built with the standard financial information. The results, obtained on a sample of Spanish companies, will also include a focus about the ability of the web features in recognizing uncommon defaulted firms.