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Abstract

In this paper, we present the design, implementation and evaluation of intelligent methods that assess bank loan applications. Assessment concerns the ability/possibility of satisfactorily dealing with loan demands. Different loan programs from different banks may be proposed according to the applicant’s characteristics. For each loan program, corresponding attributes (e.g. interest, amount of money that can be loaned) are also calculated. For these tasks, two separate intelligent systems have been developed and evaluated: 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. Neurules were produced from available patterns. Evaluation showed that performance of both systems is close although their knowledge bases were derived from different types of source knowledge.

Keywords

Expert System Fuzzy Rule Fuzzy Variable Inference Process Credit Scoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Ioannis Hatzilygeroudis
    • 1
  • Jim Prentzas
    • 2
  1. 1.Department of Computer Engineering & InformaticsUniversity of Patras, School of EngineeringPatrasGreece
  2. 2.School of Education Sciences, Department of Education Sciences in Pre-School Age, Laboratory of InformaticsDemocritus University of ThraceNea ChiliGreece

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