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Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio

Author

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  • Adela Ioana TUDOR

    (Economic Informatics Department, Academy of Economic Studies, Bucharest, Romania)

  • Adela BÂRA

    (Economic Informatics Department, Academy of Economic Studies, Bucharest, Romania)

  • Elena ANDREI (DRAGOMIR

    (Economic Informatics Department, Academy of Economic Studies, Bucharest, Romania)

Abstract

Methods underlying cluster analysis are very useful in data analysis, especially when the processed volume of data is very large, so that it becomes impossible to extract essential information, unless specific instruments are used to summarize and structure the gross information. In this context, cluster analysis techniques are used particularly, for systematic information analysis. The aim of this article is to build an useful model for banking field, based on data mining techniques, by dividing the groups of borrowers into clusters, in order to obtain a profile of the customers (debtors and good payers). We assume that a class is appropriate if it contains members that have a high degree of similarity and the standard method for measuring the similarity within a group shows the lowest variance. After clustering, data mining techniques are implemented on the cluster with bad debtors, reaching a very high accuracy after implementation. The paper is structured as follows: Section 2 describes the model for data analysis based on a specific scoring model that we proposed. In section 3, we present a cluster analysis using K-means algorithm and the DM models are applied on a specific cluster. Section 4 shows the conclusions.

Suggested Citation

  • Adela Ioana TUDOR & Adela BÂRA & Elena ANDREI (DRAGOMIR, 2012. "Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 3(2), pages 23-30, August.
    • Handle: RePEc:aes:dbjour:v:3:y:2012:i:2:p:23-30
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    References listed on IDEAS

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    1. Emilia ?I?AN & Adela Ioana TUDOR, 2011. "Conceptual and Statistical Issues Regarding the Probability of Default and Modeling Default Risk," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 2(1), pages 13-22, March.
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    Cited by:

    1. Mirela Catalina Türkes, 2017. "Cluster Analysis of Total Assets Provided By Banks from Four Continents," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 3(4), pages 24-28, December.
    2. Mirela Catalina Turkes, 2017. "Cluster Analysis Of Total Assets Provided By Banks From Four Continents," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 54-58, August.

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