ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS. Also known as connectionism, parallel distributed processing, neuro-computing and machine learning algorithms, Artificial Neural Networks (ANNs) were fi rst developed during the late 1980s and have since become a fundamental tool in combating fraud 11 . ANNs work by imitating the way the human brain learns, using complex input, hidden, and output layers.
between the input and output nodes have no interaction with the external source and become more complex in their configuration and nature depending on the complexity of the problem at hand 12 . The various nodes in each layer of the neural network are connected by edges where each edge represents a particular weight between two connected nodes. (In the human brain, these are called synapses.) The information that the neural network learns through supervised or unsupervised learning is stored in these weights. One example of the way neural networks learn is simi lar to the way children learn to recognize animals. After seeing a dog, the child can then generalize on various other breeds of dogs, categorizing and defining them as ‘dogs’ without having seen a specific breed before.
Diagram representing a feed-forward multilayer perceptron (the most common type of ANN). (Source: www.oscarkilo.net)
The input nodes retrieve in formation from an outside source (for credit card fraud detection, this would be the transactional data of a customer’s account) and the output nodes send the results from the neural networks back to the external source. The hidden nodes in-
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