Hybrid Model Binary ant ColonyAlgorithm and Support Vector Machine (BACO-SVM) for Feature Selection and Classification of Bank Customers with Case Study

Document Type : Research Paper

Authors

1 Associate Professor of Industrial Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

2 MSc Graduate of Financial Engineering, Tarbiat Modares University, Iran

Abstract

One of the most important issues faced by banks and financial institutions is the issue of credit risk. The significant amount of deferred bank claims around the world indicates the importance of this issue and the need to pay attention to it. So far, many efforts have been made to provide an effective model for evaluating and classification credit applicants as accurately as possible. In this regard, the present study attempts to provide a new approach for assessing the credit risk of bank customers. The support vector machine(SVM) method is combined as the main classifier of banking customers, with a feature selection method called the Binary Ant Colony Optimization Algorithm(BACO-SVM). In order to demonstrate the effectiveness of the proposed method, we used data from 85 companies from legal recipients of facilities of an Iranian bank in a 5 year interval (1393-1893) along with 16 characteristics related to each of them. The results of the BACO-SVM method have been compared with the PSO-SVM, GA-SVM, and SVM method. The results of the research indicated that BACO-SVM model has better performance in assessing credit risk rather than other methods. As the result, using the BACO-SVM method, we classify customers into two groups of good and bad account customers. Finally, in order to increase the flexibility in decision making, we will rank our good account customers with the VIKOR method. This rating will lead to a more accurate assessment of the credit risk situation of good account applicants.

Keywords

References

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History

  • Receive Date: 27 May 2018
  • Revise Date: 23 January 2020
  • Accept Date: 25 January 2020

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