ABSTRACT

Marked improvements in decision support sys­ tems during the last decade are a result of several contributing disciplines including operations re­ search, statistics, cognitive science and artificial in­ telligence. Of late, artificial intelligence has emerged as a strong complementary discipline to support all phases of decision making. Typical financial deci­ sion making processes in organizations are: examin­ ing loan applications, predicting bankruptcy of firms, and forecasting the investment firms financial risk. A wide range of modeling tools drawn from the sup­ porting disciplines (operations research, statistical methods and artificial intelligence) have been ap­ plied to several such problems. In the absence of a strong theory favoring one of these techniques, re-

searchers have recognized the importance of apply­ ing several of them to problems at hand. The intent of such an approach is to: (a) provide an assessment of the methods for the problem under consideration and comment on the generalizability of the results to similar problems, and (b) contribute to the develop­ ment of theory in the field that will enable decision makers to make informed selection of a model ap­ propriate for the situation.