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Intelligent systems in finance

Author

Listed:
  • Feldman Konrad
  • Treleaven Philip

Abstract

Business sectors ranging from banking and insurance to retail, are benefiting from a whole new generation of 'intelligent' computing techniques. Successful applications include asset forecasting, credit evaluation, fraud detection, portfolio optimization, customer profiling, risk assessment, economic modelling, sales forecasting and retail outlet location. The techniques include expert systems, rule induction, fuzzy logic, neural networks and genetic algorithms, which in many cases are outperforming traditional statistical approaches. Their key features include the ability to recognize and classify patterns, learning from examples, generalization, logical reasoning from premises, adaptability and the ability to handle data which is incomplete, imprecise and noisy. This paper is the first in a series to appear in Applied Mathematical Finance;here we introduce the reader to the basic concepts of intelligent systems, describe their mode of operation and identify applications of the techniques in real world problem domains. Subsequent papers will concentrate on neural networks, genetic algorithms, fuzzy logic and hybrid systems, and will investigate their history and operation more rigorously.

Suggested Citation

  • Feldman Konrad & Treleaven Philip, 1994. "Intelligent systems in finance," Applied Mathematical Finance, Taylor & Francis Journals, vol. 1(2), pages 195-207.
    • Handle: RePEc:taf:apmtfi:v:1:y:1994:i:2:p:195-207
    DOI: 10.1080/13504869400000011
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