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Artificial Intelligence (AI)

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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 T

he 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 I

NTRODUCTION · 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 communi...

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... account inquiries. The feasibility of adding phone transactions should also be considered. Cooperation among financial institutions could result in secure transfers of funds between banks when ordered by the customers over the telephone. 3. Handwriting recognition by OCR systems should be combined with existing check processing systems. These systems can reject checks that are possible forgeries. Investigators could follow-up on the OCR rejection by making appropriate inquiries with the check writer.

Bibliography

Winston, Patrick. Artificial Intelligence. Menlo Park: Addison-Wesley Publishing, 1988.

Welstead, Stephen. Neural Network and Fuzzy Logic in C/C++. New York: Welstead, 1994.

Brody, Herb. "Computers That Learn by Doing." Technology Review August 1990: 42-49.

Thompson, William. "Overturning the Category Bucket." BYTE January 1991: 249-50+.

Hinton, Geoffrey. "How Neural Networks Learn from Experience." Scientific American September 1992: 145-151.

Dreyfus, Hubert., and Stuart E. Dreyfus. "Why Computers May Never Think Like People." Technology Review January 1986: 42-61.

Shaffer, Richard. "Computers with Ears." FORBES September 1994: 238-239.
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