The data may be the results of a market research effort, or the decisions of a loan officer given a set of loan applications. Neural networks do not require the explicit coding of the problem. For example, to generate a model that performs a sales forecast, a neural network only needs to be given raw data related to the problem. The neural network sorts through this information and produces an understanding of the factors impacting sales. The model can then be called upon to provide a prediction of future sales given a forecast of the key factors.
Neural networks constitute a powerful tool for data mining.
Claims are made by several well-known investment groups that at least some of their technical analysis of financial markets and portfolio selection is performed with neural networks. Mellon Bank installed a neural network credit card fraud detection system and the realized savings were expected to pay for the new system in six months. The two main kinds of learning algorithms are supervised and unsupervised. After training, the NN is tested by giving it only input values, not target values, and seeing how close it comes to outputting the correct target values. In unsupervised learning, the NN is not provided with the correct results during training.