Current neural network technology is the most progressive of the Artificial Intelligence Essay
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 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
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 .