Fraud Detection in Credit Card Using Machine Learning

Credit card fraud has gotten worse over time, which is a serious problem that worries both customers and financial institutions. High-accuracy machine learning techniques needed to be used since conventional fraud detection methods have failed to detect fraudulent transactions. This paper provides a thorough review of machine learning methods used to identify credit card fraud. Support vector machines, which use classification techniques, and anomaly detection strategies like isolation forests are notable examples of methods that are covered. Each method’s benefits and drawbacks are carefully examined, offering insightful information for the next research projects. The findings of this study clearly demonstrate the effectiveness of machine learning algorithms in identifying credit card fraud, with some techniques obtaining impressively high-accuracy rates which can reach above 99%. Enhancing fraud detection skills via the use of machine learning will protect both consumers and financial institutions from the negative effects of fraudulent activity.