Current neural network technology is the most progressive of the artificial intelligence systems today. Applications of neural networks have made the transition from laboratory curiosities to large, successful commercial applications. To enhance the security of automated financial transactions, current technologies in both speech recognition and handwriting recognition are likely ready for mass integration into financial institutions. Computer-Aided Voice Recognition 6 The purpose of this study is to determine additional areas where artificial intelligence technology may be applied for positive identifications of individuals during financial transactions, such as automated banking transactions, telephone transactions , and home banking activities. This study focuses on academic research in neural network technology .
This study was funded by the Banking Commission in its effort to deter fraud. Recently, the thrust of studies into practical applications for artificial intelligence have focused on exploiting the expectations of both expert systems and neural network computers. In the artificial intelligence community, the proponents of expert systems have approached the challenge of simulating intelligence differently than their counterpart proponents of neural networks. Expert systems contain the coded knowledge of a human expert in a field; this knowledge takes the form of “if-then” 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 system. In the Sprint system the neural network positively recognizes the callers voice, thereby authorizing activation of his The study of the brain was once limited to the study of living tissue. Any attempts at an electronic simulation were brushed aside by the neurobiologist community as abstract conceptions that bore little relationship to reality. This was partially due to the over-excitement in the 1950s and 1960s for networks that could recognize some patterns, but were limited in their learning abilities because of hardware limitations.
In the 1990’s computer simulations of brain functions are gaining respect as the simulations increase their abilities to predict the behavior of the nervous system. This respect is illustrated by the fact that many neurobiologists are increasingly moving toward neural network type simulations. One such neurobiologist, Sejnowski, introduced a three-layer net which has made some excellent predictions about how biological systems behave. Figure 2 illustrates this network consisting of three layers, in which a middle layer of units connects the input and output layers. When the network is given an input, it sends signals through the middle layer which checks for correct output.
An algorithm used in the middle layer reduces errors by strengthening or weakening connections in the network. This system, in which the system learns to adapt to the changing conditions, is called back-propagation. The value of Sejnowski’s network is illustrated by an experiment by Richard Andersen .