Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio
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, cl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bucharest University of Economic Studies
2012-08-01
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| Series: | Database Systems Journal |
| Subjects: | |
| Online Access: | http://www.dbjournal.ro/archive/8/8_3.pdf |
| _version_ | 1797759854767505408 |
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| author | Elena ANDREI (DRAGOMIR) Adela BÂRA Adela Ioana TUDOR |
| author_facet | Elena ANDREI (DRAGOMIR) Adela BÂRA Adela Ioana TUDOR |
| author_sort | Elena ANDREI (DRAGOMIR) |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T18:50:27Z |
| format | Article |
| id | doaj.art-513942df1a9242798040ef8356271fe3 |
| institution | Directory Open Access Journal |
| issn | 2069-3230 |
| language | English |
| last_indexed | 2024-03-12T18:50:27Z |
| publishDate | 2012-08-01 |
| publisher | Bucharest University of Economic Studies |
| record_format | Article |
| series | Database Systems Journal |
| spelling | doaj.art-513942df1a9242798040ef8356271fe32023-08-02T07:15:58ZengBucharest University of Economic StudiesDatabase Systems Journal2069-32302012-08-01III22330Clustering Analysis for Credit Default Probabilities in a Retail Bank PortfolioElena ANDREI (DRAGOMIR)Adela BÂRAAdela Ioana TUDORMethods 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.http://www.dbjournal.ro/archive/8/8_3.pdfData MiningCluster AnalysisArtificial Intelligence |
| spellingShingle | Elena ANDREI (DRAGOMIR) Adela BÂRA Adela Ioana TUDOR Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio Database Systems Journal Data Mining Cluster Analysis Artificial Intelligence |
| title | Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio |
| title_full | Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio |
| title_fullStr | Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio |
| title_full_unstemmed | Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio |
| title_short | Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio |
| title_sort | clustering analysis for credit default probabilities in a retail bank portfolio |
| topic | Data Mining Cluster Analysis Artificial Intelligence |
| url | http://www.dbjournal.ro/archive/8/8_3.pdf |
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