Monitoring corporate credit risk with multiple data sources

Abstract

Purpose

Monitoring corporate credit risk (CCR) has traditionally relied on such indicators as income, debt and inventory at a company level. These data are usually released on a quarterly or annual basis by the target company and include, exclusively, the financial data of the target company. As a result of this exclusiveness, the models for monitoring credit risk usually fail to account for some significant information from different sources or channels, like the data of its supply chain partner companies and other closely relevant data yet available from public networks, and it is these seldom used data that can help unveil the immediate CCR changes and how the risk is being propagated along the supply chain. This study aims to discuss the a forementioned issues.

Design/methodology/approach

Going beyond the existing CCR prediction data, this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability.

Findings

The results show that the predictive errors of the datasets after adding supply chain data and network activity data to them are made the ever least. Moreover, intelligent algorithms like support vector machine (SVM), compared to traditionally used methods, are better at processing nonlinear datasets and mining complex relationships between multi-variable indicators for CCR evaluation.

Originality/value

This study indicates that bringing in more information of multiple data sources combined with intelligent algorithms can help companies prevent risk spillovers in the supply chain from causing harm to the company, and, as well, help customers evaluate the creditworthiness of the entity to lessen the risk of their investment.

Citation

Ni, D., Lim, M.K., Li, X., Qu, Y. and Yang, M. (2023), "Monitoring corporate credit risk with multiple data sources", Industrial Management & Data Systems, Vol. 123 No. 2, pp. 434-450. https://doi.org/10.1108/IMDS-02-2022-0091