AI Powered Credit Card Fraud Detection by Using Ensemble Method of Machine Learning

The growth of Credit Card (CC) fraud over the last few years necessitates the construction of fraud detection models that are both efficient and robust. This work investigated the use of machine learning models, especially ensemble methods, to improve the detection of CC fraud. We herein introduce an ensemble model that combines various classifiers to solve the dataset imbalance problem that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. 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 employed. The evaluation of the model is evaluated based on accuracy, precision, recall, and F1-score.