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.
TABLE OF CONTENTS
Source of Information 1
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
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 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...