Credit Card Fraud Detection With Hybrid Machine Learning Models

Credit card fraud is an ongoing issue worldwide, affecting both developed and developing countries. Increased fraudulent activities, along with evolving tactics used by fraudsters, contribute to the problem. Credit card fraud leads to financial losses for individuals and businesses, and it undermines trust in financial systems. Financial institutions and authorities face significant challenges in detecting and preventing fraud in real-time. To address the problem of credit card fraud, extensive research has been conducted in the field of machine learning and data analytics. Many studies have explored the application of machine learning techniques for fraud detection, each building upon the insights of previous research. Various machine learning models have been employed, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), XGBoost and AdaBoost, each with its strengths and weaknesses. In this particular research, the focus is on developing hybrid machine learning models for credit card fraud detection. The research aims to answer critical questions related to fraud patterns, anomalies, and early detection methods. The insights gained from data analysis, coupled with the best-performing machine learning model. By applying these advanced machine learning models, the research seeks to enhance credit card fraud detection, reduce financial losses, and bolster security measures in the financial industry. This approach benefits both financial institutions and credit cardholders by providing a robust defence against fraudulent activities.