Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio
Adela Ioana Tudor (),
Adela Bâra () and
Elena ANDREI (dragomir ()
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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
Database Systems Journal, 2012, vol. 3, issue 2, 23-30
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.
Keywords: Data Mining; Cluster Analysis; Artificial Intelligence (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:3:y:2012:i:2:p:23-30
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