Efficient Credit Card Fraud Detection System Using Big Data and Machine Learning

Radhika Chandrashekhar Dorlikar; Dr. Sudhir W. Mohod

doi:10.32628/IJSRSET2411426

Authors

Radhika Chandrashekhar Dorlikar Department of Computer Science and Engineering, BDCE, Sevagram, Wardha, Maharashtra, India Author
Dr. Sudhir W. Mohod Professor & HOD at Department of Computer Science and Engineering, BDCE, Sevagram, Wardha, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRSET2411426

Keywords:

Credit Card Fraud Detection, Big Data, Machine Learning, Anomaly Detection, Real-Time Monitoring, Data Security

Abstract

This review offers a detailed strategy to address the growing threat of credit card fraud in today's digital landscape. By utilizing Big Data analytics alongside machine learning methods, the system aims to transform fraud detection processes. It tackles the challenges arising from the increasing volume and complexity of credit card transactions, enabling the real-time detection and prevention of fraudulent actions. The system employs sophisticated machine learning algorithms to identify patterns and anomalies linked to fraudulent activities, allowing for proactive responses to emerging fraud tactics. Additionally, the system is optimized to handle and analyze large datasets efficiently, ensuring timely and precise detection of fraud. It also incorporates strong security protocols to protect sensitive customer data while adhering to privacy regulations. This review ultimately seeks to enhance the safety and reliability of electronic payments, protecting financial institutions and consumers from the harmful effects of credit card fraud.

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References

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