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444 Alternative decision-support systems
Where next?
In our view, expert systems exist as a technology with unquestionable
potential for providing financial benefits and savings through
improvement in service. The important issue, however, concerns the
identification of the most appropriate domains for such system implementation.
Choice of the wrong domain can lead to the costly waste of
time and resources, followed by disillusionment, and thence, perhaps,
the premature abandonment of a potentially profitable tool. Marketing
expert systems, thus far, do not appear to have set the world alight – the
reason for which, we have argued, is precisely this inappropriate selection
of domain.
Next, as examples, we detail two application areas that have strong
potential in financial services: (i) back-office detection of fraud and
(ii) point-of-sale advice-giving for personal financing planning. In both
cases, the benefits of expert system technology can be applied for
competitive edge.
Back-office fraud detection systems
In the back office at Chemical Bank 32 in New York an expert system
automatically flags unauthorized and suspected fraudulent transactions
in the bank’s $1 billion-a-day foreign exchange business. The rule-base
modeled in the expert system was elicited from one-to-one questioning
of Chemical Bank’s auditors – whose job it was to identify trades that
broke the law or showed poor judgment.
Chemical Bank will not reveal the actual rules in their system but
they involve monitoring the dollar volume for certain types of trades,
deviation from historical norms and other ‘disruptions’ of historically
prevalent trading patterns.
At American Express, 33 expert systems are also in place. Prior to
implementation of the expert systems, American Express’s previous
computerized authorization system handled 85% of their credit card
purchase authorizations. The remaining 15% (questionable transactions)
were forwarded to human authorizers. At the time, American
Express employed approximately 300 authorizers to handle over a
million transactions per month. This task of handling questionable
transactions required numerous activities which usually included: consulting
an individual’s credit record (which had to be called out of as
many as 13 databases) and conducting a conversation with the customer
or merchant. This gathered information would be applied to a