Business credit scoring of estonian organizations

Abstract

Recent hype in social analytics has modernized personal credit scoring to take advantage of rapidly changing non-financial data. At the same time business credit scoring still relies on financial data and is based on traditional methods. Such approaches, however, have the following limitations. First, financial reports are compiled typically once a year, hence scoring is infrequent. Second, since there is a delay of up to two years in publishing financial reports, scoring is based on outdated data and is not applied to young businesses. Third, quality of manually crafted models, although human-interpretable, is typically inferior to the ones constructed via machine learning. In this paper we describe an approach for applying extreme gradient boosting with Bayesian hyper-parameter optimization and ensemble learning for business credit scoring with frequently changing/updated data such as debts and network metrics from board membership/ownership networks. We report accuracy of the learned model as high as 99.5%. Additionally we discuss lessons learned and limitations of the approach.