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.