Credit Card Fraud Detection

Due to exponential growth in the field of online transactions, credit cards are widely used in most financial aspects and hence there are more risks of fraudulent transactions. These fraudulent transactions can be shown by analysing several behaviours of credit card users from earlier transaction history datasets. If any abnormality is noticed in the behaviour from the existing patterns, there is the possibility of fraudulent transaction. In this project the proposed will use Ensemble Learning Algorithms (XGBoost). By using these models, the proposed system will predict if the transaction is fraudulent or genuine. Therefore, by the implementation of this methodology in fraud detection systems, monetary losses which are caused due to fraudulent transactions can be decreased.