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However, if fraud occurs in small distant purchases and in large local ones, as in Figure 1, the task of classification is too complex. Interest in the approach faded for a while, but at the end of the 1970s people worked out how to tackle more complex classification tasks using networks of artificial neurons arranged in layers, so that the outputs of one layer formed the inputs of the next. The difficult part of all this is that the network has to identify the concepts to be captured in the hidden middle layer on the basis of information about how changing the weights on the links between the middle and output layers affects the final classification of transactions as fraud or bona fide. The problem is solved by computing a measure of how a change in the final set of weights changes the rate of errors in the classification and then propagating that measure backwards through the network.