Summary
The paper deals with application of MF-ARTMAP neural network on financial fraud data. The focus was on classification of data into 5 types of fraud based on the expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized with 22 features and verbal features were encoded into numerical values to be able to used them in classification procedure. The results show that in case of sufficient data (fraud) representation neural networks could be used with success and in case if there are rather small examples expert generated rules are preferred.
This project is supported by Vega Project from Ministry of Education of Slovak Republic “Intelligent Technologies in Modeling Intelligent Systems”, partially by EU Maria-Curie Individual Fellowship and also by Tatrabanka a.s. Slovakia.
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Sinčák, P., Hric, M., Val’o, R., Horanský, P., Karel, P. (2003). Financial Fraud Identification Using MF-ARTMAP Neural Networkα . In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_13
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_13
Publisher Name: Physica, Heidelberg
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