| | Bioinfo Publications | | 178 CREDIT RISK EVALUATION USING KNOWLEDGE MINING

Abstract- The Micro, Small and Medium scale Enterprises (MSME) segment is one of the fastest growing industrial segment all over the world. The researchers estimate that about 60 % of the MSME credit is provided by commercial banks alone. Over the past decade, the credit risk evaluation of MSME by banks and financial institutions has been an active area of research under the joint pressure of regula-tors and shareholders. The mathematical models of risk evaluation are at the core of modern credit risk management systems. This paper focuses on the design of expert system model for credit risk evaluation by mining the knowledge bank of Credit Rating Experts. This expert system is named as Credit Risk Evaluation Expert System (CREES). This Expert System performance resembles human expert credit risk executives thought process in decision making. CREES uses soft computing technique called Evolutionary Neuro Fuzzy logic. Authors have designed a Credit Rating Framework (CRF) comprising a large number of risk parameters parameters such as financial, business, industry and management lines. Authors have tested CREES on the selected sample data. The results obtained from CREES are compared with manual decisions to evaluate its accuracy. CREES needs to be tested using international standard German and Australian credit data sets