Credit Card Fraud Detection Using Outlier Analysis and Detection

The prevalence of electronic transactions in financial systems has significantly increased the risk of credit card fraud. Traditional fraud detection methods often struggle to keep pace with evolving fraudulent techniques. This study explores the application of outlier detection techniques as a robust approach to identifying anomalous patterns indicative of fraudulent activities in credit card transactions. Leveraging statistical and machine learning algorithms, this research aims to develop a framework capable of effectively distinguishing between genuine and fraudulent transactions by detecting outliers in transactional data. Through the analysis of various features such as transaction amount, location, time, and user behavior, the proposed method aims to enhance fraud detection accuracy while minimizing false positives. The evaluation of this approach will be conducted on real-world credit card transaction datasets to assess its efficacy in identifying fraudulent activities and its potential for integration into existing fraud prevention systems. This research endeavors to contribute to the enhancement of security measures within financial systems, mitigating financial losses, and bolstering trust in electronic payment systems.