To increase the quality of loans provision and reduce the risk involved in this process, several credit scoring models have been developed and utilized to improve the process of assessing credit worthiness. Credit scoring is an evaluation of the risk connected with lending to clients (consumers) or an organization. The GustafsonKessel (GK) algorithm has become one of the most valuable tools for credit scoring. However, this algorithm demonstrates a relatively poor capability to identify a subset of features from a large dataset. Most methods that use the GK algorithm require a predefined number of clusters.This paper presents a new GK-based modified binary particle swarm optimization (MBPSO) approach to increase the classification accuracy of the GK algorithm. The proposed MBPSO consists of three parts. First, the figure of particles is utilized to determine the optimal number of clusters automatically and overcome the drawback of the GK algorithm that requires a predefined number of c.....

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