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An adaptive sequential-filtering learning system for credit risk modeling

  • Soft computing in decision making and in modeling in economics
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A Correction to this article was published on 19 May 2021

This article has been updated

Abstract

Credit risk and business failure classification and prediction are a major topic in financial risk management and corporate finance decision making. In this work, an adaptive sequential-filtering learning system for credit risk modeling. It is basically a three-stage sequential system for credit risk and business failure classification is presented. First, different statistical filters are applied separately to perform a preselection of relevant patterns. Second, genetic algorithms are applied to preselected patterns for refinement purpose. Finally, structural risk minimization approach based on support vector machine uses refined patterns for prediction purpose. We used three credit databases and two data partition schemes: (i) random split with 80% for learning and 20% testing, and (ii) tenfold cross-validation technique. Results from all three data sets and for all partition techniques show the effectiveness of the proposed adaptive sequential-filtering learning system for credit risk modeling against single support vector machines each with specific statistical filter-based patterns. In addition, it outperformed various models validated on the same databases. It is concluded that the presented adaptive sequential system is promising for credit risk monitoring.

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Authors and Affiliations

  1. Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada

    Salim Lahmiri

  2. Department of Law and Management, School of Economics, University of Rome Tor Vergata, Rome, Italy

    Anastasia Giakoumelou

  3. Department of Economics, European University Institute, Florence, Italy

    Stelios Bekiros

  4. Rimini Centre for Economic Analysis, Wilfrid Laurier University, Waterloo, Canada

    Stelios Bekiros

Authors
  1. Salim Lahmiri
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  2. Anastasia Giakoumelou
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  3. Stelios Bekiros
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Correspondence to Salim Lahmiri.

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The original article has been updated: Due to first author name update.

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Lahmiri, S., Giakoumelou, A. & Bekiros, S. An adaptive sequential-filtering learning system for credit risk modeling. Soft Comput 25, 8817–8824 (2021). https://doi.org/10.1007/s00500-021-05833-y

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  • DOI: https://doi.org/10.1007/s00500-021-05833-y

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