A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers


  • A. G. Armaki Department of Management, Islamic Azad University Qazvin, Qazvin, Iran
  • M. F. Fallah Tehran Central Branch, Islamic Azad University, Tehran, Iran
  • M. Alborzi Information Technology Management Department, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • A. Mohammadzadeh Department of Management, Islamic Azad University Qazvin, Qazvin, Iran
Volume: 7 | Issue: 5 | Pages: 2073-2082 | October 2017 | https://doi.org/10.48084/etasr.1361


Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.


credit scoring, hybrid machine learning models, stacking, deep learning


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How to Cite

A. G. Armaki, M. F. Fallah, M. Alborzi, and A. Mohammadzadeh, “A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 5, pp. 2073–2082, Oct. 2017.


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