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 While straightforward, these methods have major limitations: they may rely on very incomplete or outdated data, and fail to capture the very dynamic environment in which the business of SMEs evolves.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.

Original languageEnglish
Title of host publicationWeb Engineering - 20th International Conference, ICWE 2020, Proceedings
EditorsMaria Bielikova, Tommi Mikkonen, Cesare Pautasso
PublisherSpringer Open
Pages113-129
Number of pages17
ISBN (Print)9783030505776
DOIs
Publication statusPublished - 2020
Event20th International Conference on Web Engineering, ICWE 2020 - Helsinki, Finland
Duration: 9 Jun 202012 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12128 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Web Engineering, ICWE 2020
CountryFinland
CityHelsinki
Period9/06/2012/06/20

ID: 79710806