Rohit Khanduri

• Financial fraud for banks can be a reason for huge monetary losses. Studies have shown that, if not mitigated, financial fraud can lead to bankruptcy for big financial institutions and even insolvency for individuals. Credit card fraud is a type of financial fraud that is ever growing. In the future, these numbers are expected to increase exponentially and that’s why a lot of researchers are focusing on machine learning techniques for detecting frauds. This task, however, is not a simple task. There are mainly two reasons • varying behaviour in committing fraud • high level of imbalance in the dataset (the majority of normal or genuine cases largely outnumbers the number of fraudulent cases) A predictive model usually tends to be biased towards the majority of samples, in an unbalanced dataset, when this dataset is provided as an input to a predictive model. In this Thesis this problem is tackled by implementing a data-level approach where different resampling methods such as undersampling, oversampling, and hybrid strategies along with bagging and boosting algorithmic approaches have been applied to a highly skewed dataset with 492 idetified frauds out of 284,807 transactions. Predictive modelling algorithms like Logistic Regression, Random Forest, and XGBoost have been implemented along with different resampling techniques to predict fraudulent transactions. The performance of the predictive models was evaluated based on Receiver Operating CharacteristicArea under the curve (AUC-ROC), Precision Recall Area under the Curve (AUC-PR), Precision, Recall, F1 score metrics.