sadatrasoul, S., gholamian, M., Siami, M., Hajimohammadi, Z. (2013). Credit scoring in banks and financial institutions via data mining techniques:
A literature review. Journal of AI and Data Mining, 1(2), 119-129. doi: 10.22044/jadm.2013.124
Seyed Mahdi sadatrasoul; Mohammadreza gholamian; Mohammad Siami; Zeynab Hajimohammadi. "Credit scoring in banks and financial institutions via data mining techniques:
A literature review". Journal of AI and Data Mining, 1, 2, 2013, 119-129. doi: 10.22044/jadm.2013.124
sadatrasoul, S., gholamian, M., Siami, M., Hajimohammadi, Z. (2013). 'Credit scoring in banks and financial institutions via data mining techniques:
A literature review', Journal of AI and Data Mining, 1(2), pp. 119-129. doi: 10.22044/jadm.2013.124
sadatrasoul, S., gholamian, M., Siami, M., Hajimohammadi, Z. Credit scoring in banks and financial institutions via data mining techniques:
A literature review. Journal of AI and Data Mining, 2013; 1(2): 119-129. doi: 10.22044/jadm.2013.124
Credit scoring in banks and financial institutions via data mining techniques:
A literature review
This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The articles are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques is also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because it’s a major issue in credit scoring problem. The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of them which are “classification and classification” and “clustering and classification” combinations are used more. Paper analysis provides a guide to future researches and concludes with several suggestions for further studies.
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