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Expert systems 439
On the basis of the comparison of the applicant to the statistical
norm, the underwriter decides whether the application is accepted,
declined, or accepted but with additional premiums or waivers of
coverage for certain conditions. The skill of underwriters appears to be
in the internalization of key heuristics about risk which enables them
to rapidly scan application forms for important phrases or indicators,
only then referring to the manuals or mortality tables which they have
available – in contrast to inexperienced underwriters who need to do
this referencing in most cases.
A number of expert systems have been built to perform the life
underwriting tasks, although these vary somewhat according to their
objectives. One system, built in collaboration with a leading UK life
insurance company, 25 was designed ostensibly as an underwriter trainer,
but with the potential for upgrading to an automated system should this
option be seen as feasible.
This system modeled the decision processes of a senior underwriter
whose thought processes matched, to a degree, the decision-making
processes documented in life insurance manuals. It also allowed junior
underwriters to be trained to the level of the modeled expert, by
requesting information from the junior underwriter on how clients had
answered the questions on their application forms, and then by justifying
its own underwriting decision through help screens.
Figure 17.1 gives the underwriting options. Bolger et al. 25 built the
system in six modules: occupation, geography, lifestyle/AIDS, financial,
hobbies and medical. Each module contained the rules that the senior
underwriter used to assess risk. Figure 17.2 presents a small example of
the rule base of the geographical module.
Note the similarity between decision tree representations in decision
analysis and the representation of the sequence of rule testing in the
expert system.
The knowledge elicitation was performed between two knowledge
engineers and one ‘expert’. Knowledge elicitation techniques included
interviews, card sorting and context focusing. 26 Card sorting consisted
of the knowledge engineer writing down on cards the names of, say,
countries. In one version of the card-sorting technique, the expert chose
three countries at random (the cards were face down) and then had to
sort them into two groups so that the countries named on two of the
cards were more similar to each other in some respect than to the third
country. In this way, the knowledge engineer was able to explore the way
in which an underwriter views countries in terms of risk dimensions.
Context focusing consisted of the knowledge engineer role playing a