Published Paper Details:

Today almost every person uses a credit card, but fraudulent activities are a great concern to both the customers and the financial institutions. This project compares the performance of several machine learning algorithms such as Decision Tree, KNN, Logistic Regression, SVM, Random Forest, and also XGBOOST, as well as a combined approach, which introduces the use of deep learning CNN. The main issue tackled is the problem on dataset which is fairly skewed, and hence, the normal transactions are a majority, while the fraudulent transactions are few. PCA for feature selection and SMOTE for data balancing techniques are applied for this purpose. A combination of the two, wherein CNN is combined with Decision Tree, increases all detection accuracy to 100%. This project offers valuable contributions as it highlights sample solutions to the problem of credit card fraud by using the Canadian Credit Card Dataset in a fast and accurate way.