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Fuzzy and Neuro-Symbolic Approaches in Personal Credit Scoring: Assessment of Bank Loan Applicants

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Book cover Innovations in Intelligent Machines-4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 514))

Abstract

Credit scoring is a vital task in the financial domain. An important aspect in credit scoring involves the assessment of bank loan applications. Loan applications are frequently assessed by banking personnel regarding the ability/possibility of satisfactorily dealing with loan demands. Intelligent methods may be employed to assist in the required tasks. In this chapter, we present the design, implementation and evaluation of two separate intelligent systems that assess bank loan applications. The systems employ different knowledge representation formalisms. More specifically, the corresponding intelligent systems are a fuzzy expert system and a neuro-symbolic expert system. The former employs fuzzy rules based on knowledge elicited from experts. The latter is based on neurules, a type of neuro-symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. A characteristic of neurules is that they retain the naturalness and modularity of symbolic rules. Neurules were produced from available patterns. Evaluation showed that the performance of both systems is close although their knowledge bases were derived from different types of source knowledge.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Informatics, School of Engineering, University of Patras, 26500, Patras, Greece

    Ioannis Hatzilygeroudis

  2. Department of Education Sciences in Pre-School Age, Laboratory of Informatics, School of Education Sciences, Democritus University of Thrace, 68100, Nea Chili, Alexandroupolis, Greece

    Jim Prentzas

Authors
  1. Ioannis Hatzilygeroudis
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  2. Jim Prentzas
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Correspondence to Ioannis Hatzilygeroudis .

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Editors and Affiliations

  1. Ecole Polytechnique Universitaire de Marseille, Aix-Marseille University (AMU), Marseille, France

    Colette Faucher

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Hatzilygeroudis, I., Prentzas, J. (2014). Fuzzy and Neuro-Symbolic Approaches in Personal Credit Scoring: Assessment of Bank Loan Applicants. In: Faucher, C., Jain, L. (eds) Innovations in Intelligent Machines-4. Studies in Computational Intelligence, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-01866-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-01866-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-01865-2

  • Online ISBN: 978-3-319-01866-9

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