Hybrid Intelligent Credit Ranking System Using Fuzzy Hybrid-Reasoning Models

The purpose 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 its 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 and fuzzy hybrid inteligence system for credit ranking using Neuo-fuzzy reasoning- transformational models. Expert system as symbolic module and Artificial Neural Network as Non-Symbolic module are components of this hybrid system research. 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 that hybrid intelligence system is more accurate and powerful in credit ranking comparing to Expert Systems.