Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

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