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Original scientific paper
https://doi.org/10.17535/crorr.2014.0010

Cluster analysis in retail segmentation for credit scoring

Sanja Scitovski ; Josip Juraj Strossmayer University of Osijek
Nataša Šarlija ; Faculty of Economics, Josip Juraj Strossmayer University of Osijek

Fulltext: english, pdf (192 KB) pages 235-245 downloads: 2.634* cite
APA 6th Edition
Scitovski, S. & Šarlija, N. (2014). Cluster analysis in retail segmentation for credit scoring. Croatian Operational Research Review, 5 (2), 235-245. https://doi.org/10.17535/crorr.2014.0010
MLA 8th Edition
Scitovski, Sanja and Nataša Šarlija. "Cluster analysis in retail segmentation for credit scoring." Croatian Operational Research Review, vol. 5, no. 2, 2014, pp. 235-245. https://doi.org/10.17535/crorr.2014.0010. Accessed 16 Sep. 2019.
Chicago 17th Edition
Scitovski, Sanja and Nataša Šarlija. "Cluster analysis in retail segmentation for credit scoring." Croatian Operational Research Review 5, no. 2 (2014): 235-245. https://doi.org/10.17535/crorr.2014.0010
Harvard
Scitovski, S., and Šarlija, N. (2014). 'Cluster analysis in retail segmentation for credit scoring', Croatian Operational Research Review, 5(2), pp. 235-245. https://doi.org/10.17535/crorr.2014.0010
Vancouver
Scitovski S, Šarlija N. Cluster analysis in retail segmentation for credit scoring. Croatian Operational Research Review [Internet]. 2014 [cited 2019 September 16];5(2):235-245. https://doi.org/10.17535/crorr.2014.0010
IEEE
S. Scitovski and N. Šarlija, "Cluster analysis in retail segmentation for credit scoring", Croatian Operational Research Review, vol.5, no. 2, pp. 235-245, 2014. [Online]. https://doi.org/10.17535/crorr.2014.0010

Abstracts
The aim of this paper is to segment retail clients by using adaptive Mahalanobis clustering in a way that each segment can be suitable for separate credit scoring development such that a better risk assessment of retail clients could be accomplished. A real data set on retail clients from a Croatian bank was used in the paper. Grouping of the data point set is carried out by using the adaptive Mahalanobis partitioning algorithm (see, e.g., [20]). It is an incremental algorithm, which recognizes ellipsoidal clusters with the main axes in the directions of eigenvectors of the corresponding covariance matrix of the data set. On the basis of the given data set, by using the well-known DIRECT algorithm for global optimization it is possible to search successively for an optimal partition with k=2, 3,... clusters. After that, a partition with the most appropriate number of clusters is determined by using various validity indexes. Based on the description of each cluster, banks could decide to develop a separate credit scoring model for each cluster as well as to create a business strategy customized to each cluster.

Keywords
cluster analysis; credit scoring; segmentation; data mining; adaptive Mahalanobis clustering; most appropriate number of clusters; data classi cation

Hrčak ID: 133699

URI
https://hrcak.srce.hr/133699

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