Network and Risk Analysis of Surety Bonds
November 07, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tamara Broderick, Ali Jadbabaie, Vanessa Lin, Manuel Quintero, Arnab Sarker, Sean R. Sinclair
arXiv ID
2511.05691
Category
q-fin.RM
Cross-listed
cs.SI,
math.OC
Citations
0
Venue
arXiv.org
Last Checked
3 months ago
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 both the average risk and the tail probability mass of the loss distribution (i.e. larger right-tail 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-fin.RM
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data
R.I.P.
π»
Ghosted
Explainable AI for Interpretable Credit Scoring
R.I.P.
π»
Ghosted
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
R.I.P.
π»
Ghosted
Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications
R.I.P.
π»
Ghosted
Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted