Default Prediction Using Network Based Features

Conference paper (2022)

Authors

L. Poenaru-Olaru Data-Intensive Systems - EEMCS
Artur Hovanesyan Exact Software
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Copyright: © 2022 L. Poenaru-Olaru, Judith Redi, Artur Hovanesyan, H. Wang
DOI: https://doi.org/10.1007/978-3-030-93409-5_60
Default prediction Network centrality Network features Network-based models Transactional network
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

Abstract

Small and medium enterprises (SME) are crucial for economy and have a higher exposure rate to default than large corporates. In this work, we address the problem of predicting the default of an SME. Default prediction models typically only consider the previous financial situation of each analysed company. Thus, they do not take into account the interactions between companies, which could be insightful as SMEs live in a supply chain ecosystem in which they constantly do business with each other. Thereby, we present a novel method to improve traditional default prediction models by incorporating information about the insolvency situation of customers and suppliers of a given SME, using a graph-based representation of SME supply chains. We analyze its performance and illustrate how this proposed solution outperforms the traditional default prediction approaches.