Contractor default prediction model for surety bonding

Contractor default is one of the major risks that threaten a project's success in the construction industry. Previous studies have focused mainly on evaluation of the contractor's financial aspects to predict contractor default. There remains a need for a comprehensive model that has the ability to incorporate the evaluation of all the project aspects, project team, contractual risks, and project management evaluation criteria to predict the possibility of a contractor's default on a specific construction project. This paper presents a contractor default prediction model (CDPM) from the surety bonding perspective that incorporates these criteria and uses a fuzzy inference system for reasoning. The CDPM provides a more objective, structured, and comprehensive approach for contractor default prediction for surety practitioners, project owners, and for self-assessment by contractors to reduce the risk of contractor default. The multi-attribute utility function was used to develop a group consensus system (GCS) to aggregate the participating experts' opinions to build the CDPM. The accuracy of the GCS was found to be 91.1%. A novel approach for fuzzy rule base development is applied to develop the rule base for the CDPM. The CDPM was validated using 30 contractor default prediction cases, and the accuracy was found to be 86.5%.