Credit scoring is an important tool to assess the solidity of small and medium-sized enterprises (SMEs), and to unlock for them new options for credit and improvement of cash flow. Credit scoring is, in its most common form, used by (potential) creditors to predict the probability of SMEs to default in the future, as an inverse measure of creditworthiness. The majority of existing credit scoring methods for SMEs are solely based on the analysis of SMEs’ financial data. While straightforward, these methods have major limitations: they may rely on very incomplete or outdated data, and fail to capture the very dynamic environment in which the business of SMEs evolves. In this paper, we propose an alternative approach to credit scoring for SMEs by enriching traditionally used financial data with social media data. We carried out our analysis on 25654 SMEs in the Netherlands, using 20 traditional financial indicators and 35 social media features. Experimental results suggest that the use of social media data in addition to traditional data significantly improves the quality of the credit scoring model for SMEs. Furthermore, we analyze the most important factors from social media data influencing the credit scoring.