Credit risk analysis using a reliability-based neural network ensemble model

Authors:
Kin Keung Lai;Lean Yu;Shouyang Wang;Ligang Zhou
Affiliations:
College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong;College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.