Dynamic Bayesian modeling for risk prediction in credit operations

Hanen Borchani, Ana Maria Martinez, Andres Masegosa, Helge Langseth, Thomas Dyhre Nielsen, Antonio Salmerón, Antonio Fernández, Anders Læsø Madsen, Ramón Sáez

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of streaming data analysis: the class labels are not immediately available and the relevant predictive features and entities under study (in this case the set of customers) may vary over time. In order to address these problems, we propose to use a dynamic classifier with a wrapper feature subset selection to find relevant features at different time steps. The proposed model is a special case of a more general framework that can also accommodate more expressive models containing latent variables as well as more sophisticated feature selection schemes.
Original languageEnglish
Title of host publicationThe 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015)
PublisherIOS Press
Publication date2015
Pages17-26
ISBN (Print)978-1-61499-588-3
ISBN (Electronic)978-1-61499-589-0
DOIs
Publication statusPublished - 2015
Event13th Scandinavian Conference on Artificial Intelligence - Halmstad University, Halmstad, Sweden
Duration: 4 Nov 20156 Nov 2015
Conference number: 13th

Conference

Conference13th Scandinavian Conference on Artificial Intelligence
Number13th
LocationHalmstad University
CountrySweden
CityHalmstad
Period04/11/201506/11/2015
SeriesFrontiers in Artificial Intelligence and Applications
Volume278
ISSN0922-6389

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Keywords

  • Streaming data
  • Dynamic Bayesian networks
  • Variational Bayes
  • Feature subset selection
  • Credit operations

Cite this

Borchani, H., Martinez, A. M., Masegosa, A., Langseth, H., Nielsen, T. D., Salmerón, A., ... Sáez, R. (2015). Dynamic Bayesian modeling for risk prediction in credit operations. In The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015) (pp. 17-26). IOS Press. Frontiers in Artificial Intelligence and Applications, Vol.. 278 https://doi.org/10.3233/978-1-61499-589-0-17
Borchani, Hanen ; Martinez, Ana Maria ; Masegosa, Andres ; Langseth, Helge ; Nielsen, Thomas Dyhre ; Salmerón, Antonio ; Fernández, Antonio ; Madsen, Anders Læsø ; Sáez, Ramón. / Dynamic Bayesian modeling for risk prediction in credit operations. The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press, 2015. pp. 17-26 (Frontiers in Artificial Intelligence and Applications, Vol. 278).
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Borchani, H, Martinez, AM, Masegosa, A, Langseth, H, Nielsen, TD, Salmerón, A, Fernández, A, Madsen, AL & Sáez, R 2015, Dynamic Bayesian modeling for risk prediction in credit operations. in The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press, Frontiers in Artificial Intelligence and Applications, vol. 278, pp. 17-26, 13th Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, 04/11/2015. https://doi.org/10.3233/978-1-61499-589-0-17

Dynamic Bayesian modeling for risk prediction in credit operations. / Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andres; Langseth, Helge; Nielsen, Thomas Dyhre; Salmerón, Antonio; Fernández, Antonio; Madsen, Anders Læsø; Sáez, Ramón.

The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press, 2015. p. 17-26 (Frontiers in Artificial Intelligence and Applications, Vol. 278).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Dynamic Bayesian modeling for risk prediction in credit operations

AU - Borchani, Hanen

AU - Martinez, Ana Maria

AU - Masegosa, Andres

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Salmerón, Antonio

AU - Fernández, Antonio

AU - Madsen, Anders Læsø

AU - Sáez, Ramón

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N2 - Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of streaming data analysis: the class labels are not immediately available and the relevant predictive features and entities under study (in this case the set of customers) may vary over time. In order to address these problems, we propose to use a dynamic classifier with a wrapper feature subset selection to find relevant features at different time steps. The proposed model is a special case of a more general framework that can also accommodate more expressive models containing latent variables as well as more sophisticated feature selection schemes.

AB - Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context of streaming data analysis: the class labels are not immediately available and the relevant predictive features and entities under study (in this case the set of customers) may vary over time. In order to address these problems, we propose to use a dynamic classifier with a wrapper feature subset selection to find relevant features at different time steps. The proposed model is a special case of a more general framework that can also accommodate more expressive models containing latent variables as well as more sophisticated feature selection schemes.

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KW - Dynamic Bayesian networks

KW - Variational Bayes

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DO - 10.3233/978-1-61499-589-0-17

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BT - The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015)

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Borchani H, Martinez AM, Masegosa A, Langseth H, Nielsen TD, Salmerón A et al. Dynamic Bayesian modeling for risk prediction in credit operations. In The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press. 2015. p. 17-26. (Frontiers in Artificial Intelligence and Applications, Vol. 278). https://doi.org/10.3233/978-1-61499-589-0-17