more data to be processed more quickly and with greater accuracy.
Recommendations
Breakthroughs in neural network technology have already created many new applications in financial transaction
security. Currently, neural network applications focus on processing data such as loan applications, and
flagging possible loan risks. As computer hardware speed increases and as neural networks get smarter,
“real-time” neural network applications should become a reality. “Real-time” processing means the network
processes the transactions as they occur.
In the mean time,
1. Watch for advances in visual recognition hardware / neural networks. When available, commercially produced
visual recognition systems will greatly enhance the security of automated financial transactions.
2. Computer aided voice recognition is already a reality. This technology should be implemented in automated
telephone 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.
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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