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Cluster Analysis for mixed data: An application to credit risk evaluation

Author

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  • Caruso, G.
  • Gattone, S.A.
  • Fortuna, F.
  • Di Battista, T.

Abstract

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.

Suggested Citation

  • Caruso, G. & Gattone, S.A. & Fortuna, F. & Di Battista, T., 2021. "Cluster Analysis for mixed data: An application to credit risk evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:soceps:v:73:y:2021:i:c:s0038012119305440
    DOI: 10.1016/j.seps.2020.100850
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    References listed on IDEAS

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    Cited by:

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    8. Mohammad Motasem ALrfai & Danilah Binti Salleh & Waeibrorheem Waemustafa, 2022. "Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan," Risks, MDPI, vol. 10(4), pages 1-11, April.

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