Automation of financial loaning system an analysis of classification algorithms on different data views

Supervised learning plays a significant role in predicting the behavior of new data, based on the rules, which are extracted keeping in view the behavior of existing data in the database. This paper is about algorithmic analysis of supervised learning for transactional data. Our main idea is to apply different classification algorithms on a preprocessed financial data set in order to evaluate that which type of classification algorithm under what sort of data model selection and with what combination of mining attributes is best suited for a transactional and frequently occurring data. In this way the algorithm with highest accuracy can be used to predict the credit rating of a client, based on his past transactions. It can be very helpful for a financial institute to develop an automated loaning system with least chance of error and fraud.