By Louis F. Pau, Dr. Claudio Gianotti (auth.)
As banks, monetary providers, insurances, and fiscal examine devices world wide attempt so as to add wisdom established features to their analyses and prone, or to create new ones, this quantity goals to supply them with concrete instruments, tools and alertness percentages. the educational portion of the booklet depends on case examine illustrations, and on resource code in many of the significant synthetic intelligence languages. The purposes similar part comprises an in depth survey of actual tasks, and a few thorough standard tools and instruments for auditing, technical research, info displays and natural-language front-ends. The examine comparable part highlights novel equipment and software program for monetary reasoning lower than uncertainty and for fusion of qualitative/quantitative model-based financial reasoning.
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Extra resources for Economic and Financial Knowledge-Based Processing
Example text
Rechenmann, Proc. , 1987, pp. 949 ff Proc. , 1985. Proc. SGAICO Symp. Commercial expert systems in banking and insurance, Lugano, 23 May 1989 La Lettre de l'intelligence artificielle, no 38, May 1988, pp. 1987, pp. 5 ff R. Reiter, FX, Proc. , London, 1986, pp. F. Pau, J. ), Expert systems in economics, banking and management, North Holland, Amsterdam 1989 Bank'AI 1988, SWIFT, London, 22-24 March 1988 J. Cuena, Proc. , 1987, pp. 981 ff A. Beerel, in: The use of expert systems in finance, London Press center, 24 Nov.
Furthermore, as explained in Section 1, methods for dealing with incompleteness and uncertainty in knowledge are the core of AI approach to problem-solving. R. R. is thus mainly concerned with quantitative knowledge: knowledge and constraints are resp. represented as numbers (or variables) and as equations capturing relations among simple data; a whole problem is then described as a set of equations (or sometimes as a set of curves in the geometric space). /DSS approach poses two main problems.
Are desirable for several reasons. 7). Experts in turn are mostly unable to express their knowledge in an organized, systematic way. As a consequence, a program development methodology is imposed based on a rapid implementation of the experts' knowledge (rapid prototyping), and on incremental refinement by comparison of human and prototype behavior when facing the same set of real-world cases. Second, AI programs either are problem-solvers, or support tools to problem-solvers. Problemsolvers directly, or through adequate support tools, ultimately interact with humans.