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Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of transactions in banking sector is rapidly growing and huge data volumes are available which represent the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating and vital area of research with the aim of extracting information from tremendous amount of accumulated data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The model has been built using data form banking sector to predict the status of loans. Three algorithms have been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the model has been implemented and tested. The results has been discussed and a full comparison between algorithms was conducted. J48 was selected as best algorithm based on accuracy.
Hamid et al. (Wed,) studied this question.