The ability to predict the financial distress of the contractor is a critical task for project
owners and various stakeholders, such as investors and banks. One of the widely used models
to predict a company’s financial distress is Altman’s Z-score. Despite its simplicity, it has never
been used to predict general contractor’s (GC) financial distress in the construction industry.
However, its original coefficients are not robust enough to make accurate predictions when the
economic circumstances change. This led to several research attempts to improve its prediction
accuracy including with the use of artificial intelligence (AI). This research proposed to
improve the Z-score accuracy by construct a general contractor business performance score.
This research employed Synthetic Minority Over-Sampling Technique (SMOTE) to deal with
the imbalanced dataset problem; Grey Relational Analysis (GRA) to rank the business
performance of contractors. Moreover, the recent Symbiotic Organism Search Neural Network
– Long Short-Term Memory (SOS NN-LSTM) is used to predict the contractor's financial
distress based on the general contractor's business performance score. The proposed model
demonstrates promising and reliable performance in predicting the general contractor's
financial distress. Furthermore, this study attempts to forecast the general contractor business
performance score for several next periods. By doing this, the proposed study will not only aid
to establish a General Contractor's Financial Distress Warning Model (GC-FDWM) to predict
the general contractor's financial distress but also warning will be delivered.

The ability to predict the financial distress of the contractor is a critical task for project
owners and various stakeholders, such as investors and banks. One of the widely used models
to predict a company’s financial distress is Altman’s Z-score. Despite its simplicity, it has never
been used to predict general contractor’s (GC) financial distress in the construction industry.
However, its original coefficients are not robust enough to make accurate predictions when the
economic circumstances change. This led to several research attempts to improve its prediction
accuracy including with the use of artificial intelligence (AI). This research proposed to
improve the Z-score accuracy by construct a general contractor business performance score.
This research employed Synthetic Minority Over-Sampling Technique (SMOTE) to deal with
the imbalanced dataset problem; Grey Relational Analysis (GRA) to rank the business
performance of contractors. Moreover, the recent Symbiotic Organism Search Neural Network
– Long Short-Term Memory (SOS NN-LSTM) is used to predict the contractor's financial
distress based on the general contractor's business performance score. The proposed model
demonstrates promising and reliable performance in predicting the general contractor's
financial distress. Furthermore, this study attempts to forecast the general contractor business
performance score for several next periods. By doing this, the proposed study will not only aid
to establish a General Contractor's Financial Distress Warning Model (GC-FDWM) to predict
the general contractor's financial distress but also warning will be delivered.

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