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 Information1
The First Steps3
Visual Recognition 4
Computer-Aided Voice Recognition6
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 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.
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.
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 neural network to interpret the signals from the electronic eye.
They modeled the network in their silicon chip based on the top three layers of
the retina which are the best understood portions of the eye.(250) These are
the photoreceptors, horizontal cells, and bipolar cells. The electronic
photoreceptors, which make up the first layer, are like the rod and cone cells
in the eye. Their job is to accept incoming light and transform it into
electrical signals. In the second layer, horizontal cells use a neural network
technique by interconnecting the horizontal cells and the bipolar cells of the
third layer. The connected cells then evaluate the estimated reliability of the
other cells and give a weighted average of the potentials of the cells around it.
Nearby cells are given the most weight and far cells less weight.(251) This
technique is very important to this process because of the dynamic nature of
image processing. If the image is accepted without testing its probable accuracy,
the likelihood of image distortion would increase as the image changed.
The silicon chip that the two professors developed contains about 2,500 pixels
photoreceptors and their associated image-processing circuitry. The chip has
circuitry that allows a professor to focus on each pixel individually or to
observe the whole scene on a monitor. The professors stated in their paper,
“The behavior of the adaptive retina is remarkably similar to that of biological
systems” (qtd in Thompon 251).
The retina was first tested by changing the light intensity of just one single
pixel while the intensity of the surrounding cells was kept at a constant level.
The design of the neural network caused the response of the surrounding pixels
to react in the same manner as in biological retinas. They state that, “In
digital systems, data and computational operations must be converted into binary
code, a process that requires about 10,000 digital voltage changes per
operation. Analog devices carry out the same operation in one step and so
decrease the power consumption of silicon circuits by a factor of about 10,000″
(qtd in Thompson 251). Besides validating their neural network, the accuracy of
this silicon chip displays the usefulness of analog computing despite the
assumption that only digital computing can provide the accuracy necessary for
the processing of information.
As close as these systems come to imitating their biological counterparts, they
still have a long way to go. For a computer to identify more complex shapes, e.
g., a person’s face, the professors estimate the requirement would be at least
100 times more pixels as well as additional circuits that mimic the movement-
sensitive and edge-enhancing functions of the eye. They feel it is possible to
achieve this number of pixels in the near future. When it does arrive, the new
technology will likely be capable of recognizing human faces.
Visual recognition would have an undeniable effect on reducing crime in
automated financial transactions. Future technology breakthroughs will bring
visual recognition closer to the recognition of individuals, thereby enhancing
the security of automated financial transactions.
Computer-Aided Voice Recognition
Voice recognition is another area that has been the subject of neural network
research. Researchers have long been interested in developing an accurate
computer-based system capable of understanding human speech as well as
accurately identifying one speaker from another.
Ben Yuhas, a computer engineer at John Hopkins University, has developed a
promising system for understanding speech and identifying voices that utilizes
the power of neural networks. Previous attempts at this task have yielded
systems that are capable of recognizing up to 10,000 words, but only when each
word is spoken slowly in an otherwise silent setting. This type of system is
easily confused by back ground noise (Moyne 100).
Ben Yuhas’ theory is based on the notion that understanding human speech is
aided, to some small degree, by reading lips while trying to listen. The
emphasis on lip reading is thought to increase as the surrounding noise levels
increase. This theory has been applied to speech recognition by adding a system
that allows the computer to view the speaker’s lips through a video analysis
system while hearing the speech.
The computer, through the neural network, can learn from its mistakes through a
training session. Looking at silent video stills of people saying each
individual vowel, the network developed a series of images of the different
mouth, lip, teeth, and tongue positions. It then compared the video images with
the possible sound frequencies and guessed which combination was best. Yuhas
then combined the video recognition with the speech recognition systems and
input a video frame along with speech that had background noise. The system
then estimated the possible sound frequencies from the video and combined the
estimates with the actual sound signals. After about 500 trial runs the system
was as proficient as a human looking at the same video sequences. This
combination of speech recognition and video imaging substantially increases the
security factor by not only recognizing a large vocabulary, but also by
identifying the individual customer using the system.
Laboratory advances like Ben Yuhas’ have already created a steadily increasing
market in speech recognition. Speech recognition products are expected to break
the billion-dollar sales mark this year for the first time. Only three years ago,
speech recognition products sold less than $200 million (Shaffer, 238). Systems
currently on the market include voice-activated dialing for cellular phones,
made secure by their recognition and authorization of a single approved caller.
International telephone companies such as Sprint are using similar voice
recognition systems. Integrated Speech Solution in Massachusetts is
investigating speech applications which can take orders for mutual funds
prospectuses and account activities (239).
Optical Character Recognition
Another potential area for transaction security is in the identification of
handwriting by optical character recognition systems (OCR). In conventional OCR
systems the program matches each letter in a scanned document with a pre-
arranged template stored in memory. Most OCR systems are designed specifically
for reading forms which are produced for that purpose. Other systems can
achieve good results with machine printed text in almost all font styles.
However, none of the systems is capable of recognizing handwritten characters.
This is because every person writes differently. Nestor, a company based in
Providence, Rhode Island has developed handwriting recognition products based on
developments in neural network computers. Their system, NestorReader,
recognizes handwritten characters by extracting data sets, or feature vectors,
from each character. The system processes the input representations using a
collection of three by three pixel edge templates (Pennisi, 23). The system
then lays a grid over the pixel array and pieces it together to form a letter.
Then the network discovers which letter the feature vector most closely matched.
The system can learn through trial and error, and it has an accuracy of about 80
percent. Eventually this system will be able to evaluate all symbols with equal
It is possible to implement new neural-network based OCR systems into standard
large optical systems. Those older systems, used for automated processing of
forms and documents, are limited to reading typed block letters. When added to
these systems, neural networks improve accuracy of reading not only typed
letters but also handwritten characters. Along with automated form processing,
neural networks will analyze signatures for possible forgeries.
Neural networks are still considered emerging technology and have a long way to
go toward achieving their goals. This is certainly true for financial
transaction security. But with the current capabilities, neural networks can
certainly assist humans in complex tasks where large amounts of data need to be
analyzed. For visual recognition of individual customers, neural networks are
still in the simple pattern matching stages and will need more development
before commercially acceptable products are available. Speech recognition, on
the other hand, is already a huge industry with customers ranging from
individual computer users to international telephone companies. For security,
voice recognition could be an added link to the chain of pre-established systems.
For example, automated account inquiry, by telephone, is a popular method for
customers to determine the status of existing accounts. With voice
identification of customers, an option could be added for a customer to request
account transactions and payments to other institutions. For credit card fraud
detection, banks have relied on computers to identify suspicious transactions.
In fraud detection, these programs look for sudden changes in spending patterns
such as large cash withdrawals or erratic spending. The drawback to this
approach is that there are more accounts flagged for possible fraud than there
are investigators. The number of flags could be dramatically reduced with
optical character recognition to help focus investigative efforts.
It is expected that the upcoming neural network chips and add-on boards from
Intel will add blinding speed to the current network software. These systems
will even further reduce losses due to fraud by enabling more data to be
processed more quickly and with greater accuracy.
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
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.
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