Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
نویسندگان
چکیده
منابع مشابه
Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation
Bart Baesens • Rudy Setiono • Christophe Mues • Jan Vanthienen Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium Department of Information Systems, National University of Singapore, Kent Ridge, Singapore 119260, Republic of Singapore Department of Applied Economic Sciences, K. U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium Department of Applied E...
متن کاملBuilding Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables
The problem of credit-risk evaluation is a very challenging and important financial analysis problem. Recently, researchers have found that neural networks perform very well for this complex and unstructured problem when compared to more traditional statistical approaches. A major drawback associated with the use of neural networks for decision making is their lack of explanation capability. Wh...
متن کاملUsing Neural Network Rule Extraction for Credit-Risk Evaluation
UFPR – *Mathematics; **Business Departments CP: 19081; CEP: 81531-990, Curitiba, Paraná, Brazil ***ITA – Computer Sciences Division , Pça. Mal. Eduardo Gomes, 50, Vl. das Acácias CEP: 12228-990, São José dos Campos, São Paulo, Brazil USP – Engineering Production , São Paulo, São Paulo, Brazil PUC-PR – Applied Informatics Graduate Program Av. Imaculada Conceição, 1155, CEP 80215-901, Curitiba, P...
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Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. I...
متن کاملCredit Risk Evaluation Using A Multilayered Feedforward Neural Network with Backpropagation Learning Rule
In this study we propose a multilayered feedforward neural network (MFNN) with Backpropagation Learning Rule Incorporating Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally p...
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ژورنال
عنوان ژورنال: Management Science
سال: 2003
ISSN: 0025-1909,1526-5501
DOI: 10.1287/mnsc.49.3.312.12739