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 expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.