# Corporate payments networks and credit risk rating

arXiv: https://arxiv.org/abs/1711.07677 · HTML: https://mlsanity.com/paper/1711.07677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance](https://mlsanity.com/paper/2204.06122/) (2022)
- [A Multimodal Approach to SME Credit Scoring Integrating Transaction and Ownership Networks](https://mlsanity.com/paper/2510.09407/) (2025)

## Abstract (arXiv)

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.