Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations
Article Metrics Report
Views, downloads, citations, engagementCited by
Current citation countResearch summary
This paper describes a new approach to financial fraud detection that is informed by both artificial intelligence and machine learning. It proposes, then tests with a simulated database of transactions, a metaheuristic algorithm based on two-layer deep learning. Employing a simple adaptation to a basic deep learning model that applies a hybridized honey badger modification, demonstrably higher quality outcomes were obtained than with commonly used competing models.
Article History
How to Cite
Stagliano, A. J.. (2024). Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations. International Journal of Business and Economic Development, Volume 12 Issue 02. https://doi.org/10.24052/IJBED/V012N02/ART-04
Citation Context
APA
Stagliano, A. J.. (2024). Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations. International Journal of Business and Economic Development, Volume 12 Issue 02. https://doi.org/10.24052/IJBED/V012N02/ART-04
MLA
Stagliano, A. J.. "Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations." International Journal of Business and Economic Development, Volume 12 Issue 02, 2024. https://doi.org/10.24052/IJBED/V012N02/ART-04
Chicago
A. J. Stagliano. "Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations." International Journal of Business and Economic Development Volume 12 Issue 02 (03 Dec 2024). https://doi.org/10.24052/IJBED/V012N02/ART-04
Harvard
Stagliano, A. J. (2024) Applying artificial intelligence and machine learning techniques in the internal audit environment: A simulation test of an improved algorithm for detection of fraudulent events in financial entity transaction processing operations. International Journal of Business and Economic Development, Volume 12 Issue 02. https://doi.org/10.24052/IJBED/V012N02/ART-04
References
- Bao, Y., B. Ke, B. Li, J. Yu, and J. Zhang (2020). “Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach,” Journal of Accounting Research, vol. 58, no. 1, pp. 199-235.
- Canhoto, A. I. and F. Clear (2020). “Artificial Intelligence and Machine Learning as Business Tools: A Framework for Diagnosing Value Destruction Potential,” Business Horizons, vol. 63, pp. 183-193.
- Fernandez-Delgado, M., E. Cernadas, S. Barro, and D. Amorim (2014). “Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?” Journal of Machine Learning Research, vol. 15, pp. 3133-3181.
- Hashim, F. A., E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany (2022). “Honey Badger Algorithm: New Metaheuristic Algorithm for Solving Optimization Problems,” Mathematics and Computers in Simulation, vol. 192, pp. 84-110.
- Hastie, T., R. Tibshirani, and J. H. Friedman (2009). The Elements of Statistical Learning. New York: Springer.
- Khoshgoftaar, T. M., J. Van Hulse, and A. Napolitano (2011). “Comparing Boosting and Bagging Techniques with Noisy and Imbalanced Data,” IEEE Transactions on Systems, Man, and Cybernetics: Part A—Systems and Humans, vol. 4, pp. 552-568.
- MIT SMR Connections (2023). How AI Changes the Rules: New Imperatives for the Intelligent Organization. Cambridge, MA: MIT Press.
- Perols, J. L., R. M. Bowen, C. Zimmermann, and B. Samba (2017). “Finding Needles in a Haystack: Using Data Analytics to Improve Fraud Detection,” The Accounting Review, vol. 92, pp. 221-245.
- Stagliano, A. J. and G. J. Tanzola (2023). “Disrupting the Accounting and Financial Reporting Functions with Implementation of Artificial Intelligence Applications,” unpublished manuscript presented at The Global Interdisciplinary Green Cities Conference 2023, University of Augsburg.
- Tuv, E. A. Borisov, G. Runger, and K. Torkkola (2009). “Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination,” The Journal of Machine Learning Research, vol. 10, pp. 1341-1366.
- Zhou, Z. H. (2012). Ensemble Learning Methods: Foundations and Algorithms. Boca Raton, FL: CRC Press.