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- W2336551258 abstract "Credit default constitutes a cornerstone for the banking industry. Effects of credit default play an important role in banking industry. Decisions concerning credits granting are one of the most crucial issues in an everyday banks' policy. Well-allocated credits may become one of the biggest sources of profits for any financial organizations. The key problem consists of distinguishing healthy (that surely repay) and delinquent (that likely default) credit applicants. Credit risk evaluation is an important and interesting management science problem in financial analysis. In this paper, the architecture used the theory of artificial neural networks and business rules to correctly determine whether a customer is default or not. The Feed-forward back propagation neural network is used to predict the credit default. The results of applying the artificial neural networks methodology to classify credit risk based upon selected parameters show abilities of the network to learn the patterns." @default.
- W2336551258 created "2016-06-24" @default.
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- W2336551258 date "2013-01-01" @default.
- W2336551258 modified "2023-09-27" @default.
- W2336551258 title "Ensemble Neural Network Strategy for Predicting Credit Default Evaluation" @default.
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