A Statistical Analytics Approach to Early Financial Risk Detection Through Transaction Monitoring

Authors

  • Md Shehab Hossain Texas A & M University-Commerce , Master’s in Business Analytics
  • Saddam Nasir Chowdhury Doctorate of Business Administration (DBA), International American University, Los Angeles, California. United States.
  • Rakesh Kumar Reddy Aduri The University of Texas at Arlington, M.Sc. Computer and Information Science, Major - Data Analysis

Keywords:

financial risk detection, transaction monitoring, statistical analytics, anomaly detection, risk scoring, early warning system

Abstract

As the volume and complexity of financial transactions have been growing, early detectionof financial risk is an important priority for banks, fintech firms and regulatory institutions.This study introduces a statistical analytics method for the identification of emergingfinancial risks in the continuous transaction monitoring. The framework includes looking attransactional variables like transaction size, transaction

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Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

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Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

European Banking Authority, & European Central Bank. (2024). 2024 report on payment fraud.

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

Financial Action Task Force. (2014). Guidance for a risk-based approach: The banking sector.

Hand, D. J. (2018). Statistical challenges of administrative and transaction data. Journal of the Royal

Statistical Society: Series A (Statistics in Society), 181(3), 555–605. https://doi.org/10.1111/rssa.12315

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., &

Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning

techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.

https://doi.org/10.1057/s41599-024-03606-0

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Journal of Computational Analysis and Applications

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Journal of Computational Analysis and Applications

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection

techniques and recent advances. Expert Systems with Applications, 193, Article 116429.

https://doi.org/10.1016/j.eswa.2021.116429

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

VOL. 35, NO. 6, 2026

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

VOL. 35, NO. 6, 2026

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic

review. Expert Systems with Applications, 240, Article 122156.

https://doi.org/10.1016/j.eswa.2023.122156

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2024-01-10

How to Cite

Md Shehab Hossain, Saddam Nasir Chowdhury, & Rakesh Kumar Reddy Aduri. (2024). A Statistical Analytics Approach to Early Financial Risk Detection Through Transaction Monitoring. Journal of Computational Analysis and Applications (JoCAAA), 33(1A), 978–993. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5789

Issue

Vol. 33 No. 1A (2024): JOCAAA

Section

Articles

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.