Credit Card Fraud Detection by Using Ensemble Method of Machine Learning

Online transactions have become an essential aspect of life as universe becomes more technological and every industry leverages the web to grow enterprises. Online transactions have been increasing steadily, and this trend is expected to continue. Credit cards are a popular form of internet transaction, but with their widespread use comes a significant drawback: credit card fraud. Since banks are unable to screen every transaction, machine learning is essential to identifying credit card fraud. In our research, we used Kaggle to gather a dataset of 2,844,808 credit card transactions from a European Bank Dataset. There are 492 fraudulent transactions in it; to balance the dataset, we proposed hybrid resampling method; and for the detection of credit card fraud, Random Forest Algorithm is used. The assessment of the model is assessed based on accuracy, precision, recall, and F1-score. Our model shown fairly good results of 97.66, 98.85, 95.94, 97.37% for accuracy, precision, recall, and F1-score, respectively.