Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms

Abstract

Credit card fraud is a major concern for both financial institutions and consumers, leading to significant financial losses and a decline in trust. With the rise in online transactions and increasingly sophisticated fraudulent schemes, there is a pressing need for strong and effective fraud detection systems. This research explores how machine learning and deep learning algorithms, particularly Random Forest (RF) and K-Nearest Neighbors (KNN), can be applied to detect credit card fraud. The main goal is to assess and compare how well these algorithms perform in accurately spotting fraudulent transactions while keeping false positives to a minimum. To carry out this research, we use a publicly available dataset of credit card transactions, which is marked by an imbalanced class distribution, where fraudulent transactions are far fewer than legitimate ones. We apply various preprocessing techniques, such as data cleaning, feature scaling, and addressing class imbalance through resampling methods like SMOTE (Synthetic Minority Over-sampling Technique), to improve data quality and model performance. Random Forest is a powerful ensemble learning method that uses a collection of decision trees to boost prediction accuracy and cut down on overfitting. K-Nearest Neighbors (KNN) is a straightforward, instance-based learning algorithm that classifies transactions by looking at the majority class of their k-nearest neighbours in the feature space. To evaluate how well both algorithms perform, we look at various metrics like precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings show that Random Forest typically outshines K-Nearest Neighbors in overall accuracy and F1-score, especially when dealing with imbalanced datasets. This research emphasizes the need to tackle class imbalance and choose the right evaluation metrics for effective fraud detection