A new generation of computing techniques, commonly known as "intelligent systems", are now being applied to a variety of financial and business modelling tasks. These techniques include genetic algorithms, neural networks, dynamical systems models (chaos theory), expert systems, rule induction, fuzzy systems and hybrids of these techniques. They are also being applied to stock market prediction, portfolio management, credit evaluation, fraud detection, insurance risk assessment, macro-economic modelling, and in many cases are outperforming traditional statistical techniques. This study analyzes the relative merits of the different techniques for financial and economic modelling, and examines current applications.
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Intelligent Systems for Finance and Business Edited by Suran Goonatilake and Philip Treleaven A new generation of computing methods, commonly known as ‘intelligent systems’ are now beginning to be successfully applied in a variety of business and financial modelling tasks, and in many cases are outperforming traditional statistical techniques. Intelligent Systems for Finance and Business provides comprehensive coverage of the latest intelligent systems including genetic algorithms, neural networks, fuzzy logic, expert systems, rule induction, genetic programming, case based reasoning and intelligent hybrid systems. The authors clearly illustrate theories with practical case studies drawn from a wide variety of business sectors such as: credit evaluation direct marketing insider dealing detection insurance fraud detection insurance claims processing financial trading portfolio management economic modelling Written by leading professionals from the US, Europe and Asia who have developed intelligent systems to tackle some of the most challenging problems in finance and business, this book will be a valuable source of information for traders, analysts, researchers and computing personnel in investment banking, retailing, marketing, financial services, insurance and regulation.
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