Banking and financial institutions are increasingly encountering the challenges of credit card fraud. Statistics suggest that each year financial institutions incur losses close to billions of dollars globally due to such frauds .Hence it is evident for financial institutions to continue to invest in advanced fraud detection systems to minimize the impact of credit card fraud on their bottom line and protect their customers from financial losses.Before deep diving into the solutions which can be proposed to solve the problem of credit card fraud , it is important to know the ways in which these frauds are taking place and what loopholes are being misused to catalyze these frauds .Hence in our research paper we first look at ways in which these frauds are taking place. Moreover, one of the other challenges to proposing a solution to this problem is the presence of highly imbalanced datasets to train the model , which motivates us to apply various techniques such as Synthetic Minority Oversampling Technique (SMOTE) to make the datasets balanced which will allow us to train the model better .We implement Artificial neural network + Recurrent neural network with auto-encoder architecture to make a model for one-class classification . The model uses these relationships to make predictions about the likelihood of fraud in new transactions. ANNs can be used to process large amounts of data and are particularly effective in detecting non-linear relationships between variables.
Vansh Sharma (Thu,) studied this question.