A Novel credit card fraud detection feature selection system using machine learning
Jitendra Kumar Chaudhary, Balakrishnan S, Dhiraj Kapila, Dler Salih Hasan, Mohammad Shafeeq, Sunil Kumar Vishwakarma · 2023
Abstract: A comprehensive overview and implementation of a credit card fraud detection system using machine learning for feature selection system. In an era where digital transactions have become the norm, the need for robust fraud detection mechanisms has never been greater. This research explores various machine learning techniques and data preprocessing methods to develop an effective and efficient system for identifying fraudulent credit card transactions. The research begins by discussing the significance of credit card fraud and its detrimental impact on financial institutions and consumers. It then delves into the fundamental concepts of machine learning and data preprocessing, providing insights into the methodologies used to tackle this complex problem. The heart of the research lies in the design and implementation of the credit card fraud detection system. The research details the selection and fine-tuning of machine learning algorithms, the creation of a reliable dataset, and the evaluation of model performance using appropriate metrics. Additionally, research discusses strategies for handling imbalanced data, a common challenge in fraud detection. Furthermore, this research addresses the ethical and privacy considerations associated with handling sensitive financial data. It emphasizes the importance of compliance with legal regulations and ethical standards while implementing such systems. This research demonstrates a practical and effective approach to credit card fraud detection using Python and machine learning.