Neural Network Technology - Security of Automated Financial Transactions

ABSTRACT 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. RESEARCH PROJECT TABLE OF CONTENTS Introduction 1 Purpose 1 Source of Information 1 Authorization 1 Overview 2 The First Steps 3 Computer-Synthesized Senses 4 Visual Recognition 4 Current Research 5 Computer-Aided Voice Recognition 6 Current Applications 7 Optical Character Recognition 8 Conclusion 9 Recommendations 10 Bibiography 11 INTRODUCTION * Purpose 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. Overview 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 don't 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 it's 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 Telephone's voice activated telephone calling system. In the Sprint system the neural network positively recognizes the caller's voice, thereby authorizing activation of his calling account. The First Steps 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 1950's and 1960's 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 at the Massachusetts Institute of Technology. Andersen's team spent years researching the neurons monkeys use to locate an object in space (Dreyfus and Dreyfus 42-61). Anderson decided to use a neural network to replicate the findings from their research. They "trained" the neural network to locate objects by retina and eye position, then observed the middle layer to see how it responded to the input. The result was nearly identical to what they found in their experiments with monkeys. Computer-Synthesized Senses * Visual Recognition The ability of a computer to distinguish one customer from another is not yet a reality. But, recent breakthroughs in neural network visual technology are bringing us closer to the time when computers will positively identify a person. * Current Research Studying the retina of the eye is the focus of research by two professors at the California Institute of Technology, Misha A. Mahowald and Carver Mead. Their objective is to electronically mimic the function of the retina of the human eye. Previous research in this field consisted of processing the absolute value of the illumination at each point on an object, and required a very powerful computer.(Thompson 249-250). The analysis required measurements be taken over a massive number of sample locations on the object, and so, it required the computing power of a massive digital computer to analyze the data. The professors believe that to replicate the function of the human retina they can use a neural network modeled with a similar biological structure of the eye, rather than simply using massive computer power. Their chip utilizes an analog computer which is less powerful than the previous digital computers. They compensated for the reduced computing power by employing a far more sophisticated