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Applying AI to Finance. The Symbolic and Sub-Symbolic approaches.
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Recent years have seen a broadening of the array of computer technologies applied to finance, showing that the diversity and richness of the applications of AI addressed to solve problems in finance is very successful and developing.
Solving complex problems and dealing with uncertainty, such as financial investment planning, foreign exchange trading, and knowledge discovery from large/multiple databases, involves many different components or sub-tasks, each of which requires different types of processing. To solve such complex problems, a great diversity of intelligent techniques are required, which can be divided into two approaches: symbolic, which includes traditional hard computing techniques such as expert systems and sub-symbolic, which includes soft computing techniques such as fuzzy logic, neural networks, and genetic algorithms
Each technique has particular strength and limitations, and cannot be successfully applied to every type of problem. Moreover, some of the techniques are complementary in many aspects, so they can mutually compensate weaknesses and alleviate inherent problems. These results in systems called hybrid intelligent systems, which have recently begun to gain prominence as a potential tool in solving a wide variety of complex tasks. According to Zahedi (1993), expert systems and ANN offer qualitative methods for
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