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Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

Author

Listed:
  • Agustin Pérez-Martín

    (Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain
    These authors contributed equally to this work.)

  • Agustin Pérez-Torregrosa

    (Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain
    These authors contributed equally to this work.)

  • Alejandro Rabasa

    (Operations Research Center, Miguel Hernández University of Elche, 03202 Elche, Spain
    These authors contributed equally to this work.)

  • Marta Vaca

    (Economic and Financial Studies Department, Miguel Hernández University of Elche, 03202 Elche, Spain
    These authors contributed equally to this work.)

Abstract

Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time.

Suggested Citation

  • Agustin Pérez-Martín & Agustin Pérez-Torregrosa & Alejandro Rabasa & Marta Vaca, 2020. "Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1971-:d:440863
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    References listed on IDEAS

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    1. Y Liu & M Schumann, 2005. "Data mining feature selection for credit scoring models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1099-1108, September.
    2. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    3. Lean Yu, 2014. "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-9, June.
    4. Pérez-Martín, A. & Pérez-Torregrosa, A. & Vaca, M., 2018. "Big Data techniques to measure credit banking risk in home equity loans," Journal of Business Research, Elsevier, vol. 89(C), pages 448-454.
    5. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, March.
    6. Finlay, Steven, 2011. "Multiple classifier architectures and their application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 210(2), pages 368-378, April.
    7. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    8. Steenackers, A. & Goovaerts, M. J., 1989. "A credit scoring model for personal loans," Insurance: Mathematics and Economics, Elsevier, vol. 8(1), pages 31-34, March.
    9. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    10. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    11. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research, Elsevier, vol. 195(3), pages 942-959, June.
    12. M. Almiñana & L. Escudero & A. Pérez-Martín & A. Rabasa & L. Santamaría, 2014. "A classification rule reduction algorithm based on significance domains," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 397-418, April.
    13. Ochoa P., Juan Camilo & Galeano M., Wilinton & Agudelo V., Luis Gabriel, 2010. "Construcción de un modelo de scoring para el otorgamiento de crédito en una entidad financiera," Perfil de Coyuntura Económica, Universidad de Antioquia, CIE, November.
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    1. Wangwang Yan & Jing Ba & Taihua Xu & Hualong Yu & Jinlong Shi & Bin Han, 2022. "Beam-Influenced Attribute Selector for Producing Stable Reduct," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    2. Maria Patricia Durango‐Gutiérrez & Juan Lara‐Rubio & Andrés Navarro‐Galera, 2023. "Analysis of default risk in microfinance institutions under the Basel III framework," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1261-1278, April.

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