Assessing stakeholder’s credit risk using data mining in construction project

• Main Authors: Zakaria, NurHafizah, Mohd Shaharanee, Izwan Nizal, Jamil, Jastini, Mohd Nawi, Mohd Nasrun
• Format: Article
• Language: English
• Published: AENSI Journals 2015
• Subjects: QA76 Computer software; TH Building construction
• Online Access: http://repo.uum.edu.my/13881/1/Assess.pdf; http://repo.uum.edu.my/13881/; http://www.aensiweb.com/AEB/
• Institution: Universiti Utara Malaysia

Nowadays, the rapid growth of national and global economic demands an efficient,innovative and cost effective for building and infrastructure project. Partnering in construction projects are complex in nature due to human and non-human factors variable.For instance, credit capacity is a common attribute from client’s perspectives when selecting partners in their construction project. However, the assessment of the credit risk capacity of partners (such as project manager, quantity surveyor, consultant, and contractor) is neglected particularly involving design build projects in Malaysia.Due to unforeseen risk associated to credit capacity, project delay and cost overrun occur frequently in Malaysian construction industry.Thus, this research aims to develop a framework for accessing credit risk using data mining for design build project. This study will employ case study approach in order to gather information, develop data mining model and validation with real case projects involving public clients.The framework will enable public client to select appropriate partners for their construction project with minimal risk. It is anticipated that this study will yield an efficient artifact to improve the existing government procurement system such as ePerolehan and e-Perunding.