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Investigating Innovative Approaches to Identify Financial Fraud in Real-Time
Corresponding Author(s) : Tanvir Rahman Akash
American Journal of Economics and Business Management,
Vol. 7 No. 11 (2024): November
Abstract
Financial fraud poses a significant threat to global economies, costing businesses and individuals billions annually. With the rise of digital transactions, traditional methods of fraud detection are no longer sufficient. This paper explores cutting-edge approaches to real-time financial fraud detection, including artificial intelligence (AI), machine learning (ML), blockchain technology, and behavioral analytics. Through an in-depth analysis of their capabilities and limitations, we highlight how these approaches enable organizations to mitigate fraud risks effectively while maintaining operational efficiency. We also provide data-driven insights into detection rates, cost efficiency, and industry-specific challenges, supported by extensive case studies and real-world applications.
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- Levi, M., Burrows, J., Fleming, M. H., & Hopkins, M. (2020). "The Role of AI in Financial Fraud Detection." Journal of Financial Crime, 27(4), 1120-1140.
- Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2018). "Blockchain Challenges and Opportunities: A Survey." International Journal of Web and Grid Services, 14(2), 352-375.
- Association of Certified Fraud Examiners (ACFE). (2022). "Report to the Nations: Global Study on Occupational Fraud and Abuse."
- Apache Software Foundation. (2022). "Apache Kafka Documentation." Retrieved from https://kafka.apache.org.
- Spark Streaming Documentation. (2022). "Real-Time Analytics with Apache Spark." Retrieved from https://spark.apache.org.
- Zhang, Y., Li, J., & Xu, Z. (2019). "Enhancing Fraud Detection with Behavioral Analytics." IEEE Transactions on Big Data, 7(3), 480-490.
- Cavoukian, A. (2011). "Privacy by Design: The 7 Foundational Principles." Information and Privacy Commissioner of Ontario, Canada.
- Kairouz, P., McMahan, B., et al. (2019). "Advances and Open Problems in Federated Learning." arXiv preprint arXiv:1912.04977.
- Veale, M., & Binns, R. (2017). "Fairer Machine Learning through Privacy-Preserving Technologies." IEEE Data Engineering Bulletin.
- Zhang, J., & Yang, H. (2020). "Big Data and Real-Time Analytics for Financial Security." Journal of Applied Data Science, 2(4), 123-139.
References
Levi, M., Burrows, J., Fleming, M. H., & Hopkins, M. (2020). "The Role of AI in Financial Fraud Detection." Journal of Financial Crime, 27(4), 1120-1140.
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2018). "Blockchain Challenges and Opportunities: A Survey." International Journal of Web and Grid Services, 14(2), 352-375.
Association of Certified Fraud Examiners (ACFE). (2022). "Report to the Nations: Global Study on Occupational Fraud and Abuse."
Apache Software Foundation. (2022). "Apache Kafka Documentation." Retrieved from https://kafka.apache.org.
Spark Streaming Documentation. (2022). "Real-Time Analytics with Apache Spark." Retrieved from https://spark.apache.org.
Zhang, Y., Li, J., & Xu, Z. (2019). "Enhancing Fraud Detection with Behavioral Analytics." IEEE Transactions on Big Data, 7(3), 480-490.
Cavoukian, A. (2011). "Privacy by Design: The 7 Foundational Principles." Information and Privacy Commissioner of Ontario, Canada.
Kairouz, P., McMahan, B., et al. (2019). "Advances and Open Problems in Federated Learning." arXiv preprint arXiv:1912.04977.
Veale, M., & Binns, R. (2017). "Fairer Machine Learning through Privacy-Preserving Technologies." IEEE Data Engineering Bulletin.
Zhang, J., & Yang, H. (2020). "Big Data and Real-Time Analytics for Financial Security." Journal of Applied Data Science, 2(4), 123-139.