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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 business and economic systems that traditional quantitative tools in statics and econometrics cannot quantify due to complexity in translating the systems into precise mathematical functions.

Expert systems Advantages and disadvantages

To begin with, the most used Symbolic approach to AI methods in financial field have been expert systems which deal best in the field of financial analysis.

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  • Preparation and analysis of reports

As mentioned above ES are very good at financial analysis field. CoverStory is an expert system developed by IRI to tackle the problem of too much data. Ocean Spray has been a development partner and first client. CoverStory automates the creation of summary memoranda for reports extracted from large scanner databases. The goal is to provide a cover memo, like the one a marketing analyst would write, to describe key events that are reflected in the database - especially in its newest numbers. The project began as a teaching exercise in marketing science - "How would you summarize what is important in this data?" (Stoyiannidis, 1987; Little, 1988) - and has developed into a practical tool. CoverStory is a particularly desirable development because, with very little effort, it provides users with top line summaries and analyses across a wide variety of situations. Previously this required time-consuming intervention by a skilled analyst. Furthermore the technology is an extensible platform on which to build increasingly sophisticated decentralized analysis for the user community.[1]

  • Appraising loan applications[2]

ALEES – an agricultural loan evaluation expert system that incorporates qualitative factors such as a loan officer’s intuition, experience and judgement as well as quantitative factors (Bryant 2001)[3] In this case the major advantage of the ES is time saved -- a decision can be obtained from it in 30 seconds that

ANN Strengths and Weaknesses[4]

The most used Sub-Symbolic approach to AI is Artificial Neural Networks (ANN), which deals best with uncertainty, as they could be seen as information processing systems which use learning and generalization capabilities and are very adaptive.

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The motives of interest for combining rule-based and neural computations in expert systems are:[5]

  1. Technique enhancement. Automatic data acquisition for expert systems may require kinds of sensory processing that are effectively dealt with by neural nets. In turn, hypothetical reasoning is often called for in interpreting and classifying sensory inputs. Thus, adaptive neural nets detecting perceptual clues and rule-based computations performing interpretative reasoning can fruitfully cooperate in this area.
  2. Realising multifunctioning. By ‘reversing’ the neural computations applied in classification tasks, one may obtain instances of the classes that neural nets were trained to classify. In this way the information can be exploited to provide nonsymbolic forms of explanation in expert systems.
  3. Multiplicity of application tasks. The hardware implementation of parallel processing models of propositional rule systems can make the difference when expert systems are required to make very fast decisions.

Hybrid system examples


[1] http://dspace.mit.edu/bitstream/handle/1721.1/47088/coverstoryautoma00schm.pdf?sequence=1




[5] Expert Systems. The technology of knowledge management and decision making for 21st century. Volume 5. Cornelius T. Leondes




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