Published Paper Details:

Banking industry has the major activity of lending money to those who are in need of money. In order to payback the principle borrowed from the depositor bank collects the interest made by the principle borrowers. Credit risk analysis is becoming an important field in financial risk management. Many credit risk analysis techniques are used for the evaluation of credit risk of the customer dataset. The evaluation of the credit risk datasets leads to the decision to issue the loan of the customer or reject the application of the customer is the difficult task which involves the deep analysis of the customer credit dataset or the data provided by the customer. In this paper we are surveying different techniques for the credit risk analysis which are used for the evaluation for the credit risk datasets. Credit card fraud is a serious problem in financial services. Machine learning algorithm based fraud detection scheme is implemented for detect the fraud card. The methods which use long short- term memory (LSTM) networks and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed rate.