Using the Hybrid Model for Credit Scoring (Case Study: Credit Clients of microloans, Bank Refah-Kargeran of Zanjan, Iran)

Document Type: Original Manuscript

Authors

1 Department of Management, Alborz College, University of Tehran, Tehran, Iran

2 Faculty of Management, University of Tehran, Tehran, Iran

3 Faculty of Management, University of Tehran, Tehran, Iran.

10.22094/joie.2019.574793.1583

Abstract

In any country, commercial banks lay the groundwork for economic growth by collecting national resources and capitals and allocating them to different economic sectors. Optimal allocation of resources is especially important in achieving this goal. Banks with an effective and dynamic system of customer assessment can efficiently allocate their resources to customers regardless of their geographic area. Following[M1]  a linear programming optimization approach, this research employs the UTilités Additives DIScriminantes (UTADIS) model for credit scoring of bank customers. The advantages of the proposed technique are high flexibility, mutual interaction with decision makers, and the ability to update under various macroeconomic conditions. The chosen environment is a branch of Bank Refah Kargaran, one of the popular banks in Iran. According to the experimental results, the proposed technique demonstrates high effectiveness. Also, the results indicate that the initial credit score and age of the applicants are the most influential factors for credit scoring of customers.

Graphical Abstract

Using the Hybrid Model for Credit Scoring (Case Study: Credit Clients of microloans, Bank Refah-Kargeran of Zanjan, Iran)

Highlights

  • Proposing a credit scoring model based on the UTADIS model
  • Better effectiveness compared to nine machine learning based state -of-the-art techniques
  • Solving a numerical example to validate and illustrate the credit scoring model
  • Based on the results, credit score and customer age are the most influential variables
  • Specifying the cut -off points for each class of customers

Keywords

Main Subjects


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