rules. The problem with this approach is that people dont always know why they do what they do. And even when they can express this knowledge, it is not easily translated into usable computer code. Also, expert systems are usually bound by a rigid set of inflexible rules which do not change with experience gained by trail and error. In contrast, neural networks are designed around the structure of a biological model of the brain. Neural networks are composed of simple components called "neurons" each having simple tasks, and simultaneously communicating with each other by complex interconnections. As Herb Brody states, "Neural networks do not require an explicit set of rules. The network - rather like a child - makes up its own rules that match the data it receives to the result its told is correct" (42). Impossible to achieve in expert systems, this ability to learn by example is the characteristic of neural networks that makes them best suited to simulate human behavior. Computer scientists have exploited this system characteristic to achieve breakthroughs in computer vision, speech recognition, and optical character recognition. Figure 1 illustrates the knowledge structures of neural networks as compared to expert systems and standard computer programs. Neural networks restructure their knowledge base at each step in the learning process. This paper focuses on neural network technologies which have the potential to increase security for financial transactions. Much of the technology is currently in the research phase and has yet to produce a commercially available product, such as visual recognition applications. Other applications are a multimillion dollar industry and the products are well known, like Sprint Telephones voice activated telephone calling syst...
