A Credit Scoring Model for SMEs Based on Social Media Data

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 While straightforward, these methods have major limitations: they may rely on very incomplete or outdated data, and fail to capture the very dynamic environment in which the business of SMEs evolves.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

[1]  E. Altman,et al.  Modelling Credit Risk for SMEs: Evidence from the U.S. Market , 2007 .

[2]  Marijana Zekic-Susac,et al.  Modelling small-business credit scoring by using logistic regression, neural networks and decision trees , 2005, Intell. Syst. Account. Finance Manag..

[3]  Younes Boujelbene,et al.  Predicting credit risk on the basis of financial and non-financial variables and data mining , 2018, Review of Accounting and Finance.

[4]  Raymond Y. K. Lau,et al.  Mining Individuals' Behavior Patterns from Social Media for Enhancing Online Credit Scoring , 2018, PACIS.

[5]  Yufei Xia,et al.  A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring , 2017, Expert Syst. Appl..

[6]  Sanghoon Kim,et al.  Improved technology scoring model for credit guarantee fund , 2005, Expert Syst. Appl..

[7]  Mo Hai,et al.  Research on Credit Scoring by Fusing Social Media Information in Online Peer-to-Peer Lending , 2016 .

[8]  Ron J. Feldman.  Small business loans, small banks and big change in technology called credit scoring , 1997 .

[9]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[10]  Margaret McCann,et al.  Use and measurement of social media for SMEs , 2015 .

[11]  Grid Thoma,et al.  Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures , 2013 .

[12]  Francesco Ciampi,et al.  Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises , 2013 .

[13]  N. Michaelidou,et al.  Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands , 2011 .

[14]  Alessandro Bozzon,et al.  Social Glass: A Platform for Urban Analytics and Decision-making Through Heterogeneous Social Data , 2015, WWW.

[15]  So Young Sohn,et al.  A Credit Scoring Model for SMEs Based on Accounting Ethics , 2017 .

[16]  Enrique Bonsón,et al.  A set of metrics to assess stakeholder engagement and social legitimacy on a corporate Facebook page , 2013, Online Inf. Rev..

[17]  Ibrahim S. Karakadilar,et al.  Exploring the Role of Social Media for SMEs: As a New Marketing Strategy Tool for the Firm Performance Perspective , 2014 .

[18]  Christophe Mues,et al.  An experimental comparison of classification algorithms for imbalanced credit scoring data sets , 2012, Expert Syst. Appl..

[19]  David Martens,et al.  Who cares about your Facebook friends? Credit scoring for microfinance , 2015 .

[20]  Silvia Angilella,et al.  The financing of innovative SMEs: A multicriteria credit rating model , 2013, Eur. J. Oper. Res..

[21]  Tuan Phan,et al.  Social Media-Driven Credit Scoring : the Predictive Value of Social Structures Research-in-Progress , 2016 .

[22]  Edward I. Altman,et al.  Assessing the credit worthiness of Italian SMEs and mini-bond issuers , 2020 .

[23]  Michael D. Barnes,et al.  Use of Social Media in Health Promotion , 2012, Health promotion practice.

[24]  R. O. Edmister,et al.  JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS March 1972 AN EMPIRICAL TEST OF FINANCIAL RATIO ANALYSIS FOR SMALL BUSINESS FAILURE PREDICTION , 2009 .

[25]  Alessandro Bozzon,et al.  Linking Accounts across Social Networks: the Case of StackOverflow, Github and Twitter , 2015, KDWeb.

[26]  Chi Xie,et al.  Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach , 2019, International Journal of Production Economics.

[27]  José Salvador Sánchez,et al.  On the suitability of resampling techniques for the class imbalance problem in credit scoring , 2013, J. Oper. Res. Soc..

[28]  Edward I. Altman,et al.  An emerging market credit scoring system for corporate bonds , 2005 .

[29]  Nick Wilson,et al.  The value of non-financial information in small and medium-sized enterprise risk management , 2010 .

[30]  David Martens,et al.  What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance , 2019, J. Oper. Res. Soc..

[31]  Alexey Masyutin.  Credit scoring based on social network data , 2015 .