Network and Risk Analysis of Surety Bonds

TL;DR

This paper models the contractor network in surety bonds as a directed graph to analyze how risk propagates, showing that network effects increase overall risk exposure, validated with real insurance data.

Contribution

It introduces a network-based risk assessment model for surety bonds, extending existing models with a stochastic process and theoretical analysis of risk propagation.

Findings

Network effects increase average risk for surety organizations.

Incorporating network effects results in approximately 2% higher risk exposure.

The model is validated using real data from an insurance company.

Abstract

Surety bonds are financial agreements between a contractor (principal) and obligee (project owner) to complete a project. However, most large-scale projects involve multiple contractors, creating a network and introducing the possibility of incomplete obligations to propagate and result in project failures. Typical models for risk assessment assume independent failure probabilities within each contractor. However, we take a network approach, modeling the contractor network as a directed graph where nodes represent contractors and project owners and edges represent contractual obligations with associated financial records. To understand risk propagation throughout the contractor network, we extend the celebrated Friedkin-Johnsen model and introduce a stochastic process to simulate principal failures across the network. From a theoretical perspective, we show that under natural monotonicity conditions on the contractor network, incorporating network effects leads to increases in the average risk for the surety organization. We further use data from a partnering insurance company to validate our findings, estimating an approximately 2% higher exposure when accounting for network effects.