Limitations of Expert Systems however are that they require considerable storage space and rely heavily on extensive programming of expert human knowledge in order to make decisions. Some experts believe that by using a rule-based system with neural networks, the performance of detecting fraudulent activity increases 21 .
BAYESIAN NEURAL NETWORKS. These types of networks take a slightly different approach to the general guidelines and rules of learning that are commonly seen in ANNs and FNNs. Typically, Bayesian Neural Networks use Naive Bayesian Classifiers , a simple method of classification, to classify transaction activity. Bayesian learning can be trained very efficiently in a supervised learning setting and uses probability to represent uncertainty about relationships that have been learnt as opposed to variations on maximum l ikelihood estimation 22 . Where neural networks try to find a set of weights for each node (process of learning) to best fit the data inputted, Bayesian learning makes prior predictions by means of probability
distribution over the network weights as to what the true relationship might be 23 . One study looked at the comparison of using both ANNs and Bayesian Belief Network algorithms in fraud detection, and found that the use of Bayesian Neural Networks, although slower, were in fact more accurate than the use of ANNs alone 24 . In fact, many believe the use of Bayesian methods to be highly effective in real world data sets as they offer better predictive accuracy 25 . This is supported by research which concluded that the use of Bayesian Neural Networks were far superior and accurate in detecting credit card transactional fraud than Naive Bayesian Classifier 26 .
Made with FlippingBook Digital Publishing Software