Corporate payments networks and credit risk rating

TL;DR

This paper investigates how the topology of corporate payment networks correlates with firms' credit risk, revealing risk homophily and enabling risk prediction based solely on network properties.

Contribution

It provides the first large-scale analysis linking network topology with credit risk and demonstrates the potential for risk prediction using network features.

Findings

Significant correlation between local network properties and firm risk.

Evidence of risk homophily among firms in the payment network.

Network-based risk prediction is feasible without direct credit rating data.

Abstract

Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with the topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node.