Abstract
We apply neural and wavelet network architectures to publicly available financial data to match the credit ratings of insurance companies. The main aim is to assess whether wavelet networks are likely to provide sufficiently improved results to justify further work. We consider three aspects when comparing the networks: complexity, predictive accuracy and prediction confidence.
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© 2003 Springer-Verlag Berlin Heidelberg
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Prigmore, M., Long, J.A. (2003). A Comparison of the Effectiveness of Neural and Wavelet Networks for Insurer Credit Rating Based on Publicly Available Financial Data. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_53
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DOI: https://doi.org/10.1007/3-540-45034-3_53
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