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Neural Networks: A New Tool for Predicting Thrift Failures *
Author(s) -
Salchenberger Linda M.,
Cinar E. Mine,
Lash Nicholas A.
Publication year - 1992
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1992.tb00425.x
Subject(s) - artificial neural network , computer science , econometrics , artificial intelligence , machine learning , data mining , economics
A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.