Credit Scoring Prediction using Graph Features

Abstract

Small and medium enterprises (SMEs) bring a significant contribution to each country’s economy, ensuring both a high employment rate and financial prosperity. Despite their essential role, these type of companies presents a higher vulnerability to default than large corporates. The default event implies that the SME could not properly reimburse the money owned to its suppliers. If their default could be forecast, experts could adopt measures in order to prevent it or its impactful consequences. For this reason, developing a credit scoring model which is able to predict which SMEs are endangered of default is crucial. There are plenty of credit scoring prediction models available in the literature. However, most of them are only relying on the financial status of one company. In this work, we are presenting a novel method for credit scoring prediction which does not only take into account the SMEs’ financial situation, but also their position and role within a transactional network. A transactional network is a graph, whereby the nodes are represented by SMEs and the edges show that between two nodes there should be at least one transaction. In our work, we highlight the limitations that traditional models face and provide an alternative to overcome them. Furthermore, our findings show that combining network features with financial features could lead to a more accurate prediction and increase the robustness of the model. For this reason, we believe that the transactional network carries significant insights and could be a meaningful addition to financial based credit scoring prediction models.