Using AWS SageMaker to Deploy ML Credit Card Fraud Detection Model

Credit card fraud remains a persistent and costly challenge within the financial industry, necessitating the development of robust and efficient solutions for detection and prevention. Machine learning has emerged as a powerful tool for identifying fraudulent transactions, offering the potential to save financial institutions and consumers billions of dollars annually. This research study explores the practical application of Amazon Web Services (AWS) SageMaker, a comprehensive machine learning platform, to deploy an effective fraud detection system for credit cards. The study commences with a comprehensive review of existing literature on credit card fraud detection methods, shedding light on the limitations faced by conventional approaches. Subsequently, an innovative approach is presented, encompassing key stages such as feature engineering, data preparation, and the implementation of diverse machine learning techniques. To harness the scalability, deployment simplicity, and cost-effectiveness of AWS SageMaker, a detailed guide is provided through the entire model development and deployment process on the platform. Research findings demonstrate the efficacy of the machine learning model in accurately identifying fraudulent credit card transactions. A thorough analysis of the model's performance is presented, including metrics such as accuracy, precision, recall, F1-score, and ROC AUC. Additionally, a discussion is included on the advantages and considerations of utilizing AWS SageMaker for deploying machine learning models in real-world scenarios involving fraud detection. This research study aims to furnish financial institutions, data scientists, and researchers with valuable insights into effectively leveraging AWS SageMaker for credit card fraud detection. The conclusion emphasizes the significance of this approach in fortifying security measures and mitigating financial losses stemming from fraudulent activities.