Abstract
Integration of explainable Artificial Intelligence (XAI) methodologies into compliance frameworks represents a considerable potential for augmenting fraud prevention strategies across diverse sectors. This paper explores the role of explainable AI in compliance models for fraud prevention. In highly regulated sectors like finance, healthcare, and cybersecurity, XAI helps identify abnormal behaviour and ensure regulatory compliance by offering visible and comprehensible insights into AI-driven decision-making processes. The findings indicate the extent to which XAI can improve the efficacy, interpretability, and transparency of initiatives aimed at preventing fraud. Stakeholders can comprehend judgements made by AI, spot fraudulent tendencies, and rank risk-reduction tactics using XAI methodologies. In addition, it also emphasizes how crucial interdisciplinary collaboration is to the advancement of XAI and its incorporation into compliance models for fraud detection across multiple sectors. In conclusion, XAI in compliance models plays a vital role in fraud prevention. Therefore, through the utilization of transparent and interpretable AI tools, entities can strengthen their ability to withstand fraudulent operations, build trust among stakeholders, and maintain principles within evolving regulatory systems.
References
Akindote, O. J., Abimbola Oluwatoyin Adegbite, Samuel Onimisi Dawodu, Adedolapo Omotosho, Anthony Anyanwu, & Chinedu Paschal Maduka. (2023). Comparative review of big data analytics and GIS in healthcare decision-making. World Journal of Advanced Research and Reviews, 20(3), 1293–1302. https://doi.org/10.30574/wjarr.2023.20.3.2589
Al-Anqoudi, Y., Al-Hamdani, A., Al-Badawi, M., & Hedjam, R. (2021). Using Machine Learning in Business Process Re-Engineering. Big Data and Cognitive Computing, 5(4), 61. https://doi.org/10.3390/bdcc5040061
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Ser, J. D., Díaz-Rodríguez, N., & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99(101805), 101805. sciencedirect. https://doi.org/10.1016/j.inffus.2023.101805
Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2021). Explaining anomalies detected by autoencoders using Shapley Additive Explanations. Expert Systems with Applications, 186, 115736. https://doi.org/10.1016/j.eswa.2021.115736
Arrieta, B. A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58(1), 82–115. https://arxiv.org/pdf/1910.10045.pdf
Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine Learning Explainability in Finance: An Application to Default Risk Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3435104
Buhrmester, V., Münch, D., & Arens, M. (2021). Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey. Machine Learning and Knowledge Extraction, 3(4), 966–989. https://doi.org/10.3390/make3040048
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable AI in Fintech Risk Management. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00026
Confalonieri, R., Prado, del, Sebastia Agramunt, Malagarriga, D., Faggion, D., Tillman Weyde, & Besold, T. R. (2019). An Ontology-based Approach to Explaining Artificial Neural Networks. ArXiv (Cornell University).
Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114. https://doi.org/10.1016/j.eswa.2019.113114
Dhanorkar S., Wolf, C. T., Qian, K., Xu, A., Popa, L., & Li, Y. (2021). Who needs to know what, when? Broadening the Explainable AI (XAI) Design Space by Looking at Explanations Across the AI Lifecycle. Designing Interactive Systems Conference 2021. https://doi.org/10.1145/3461778.3462131
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896. https://doi.org/10.1016/j.inffus.2023.101896
Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2021). Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w
Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial Risk Management and Explainable, Trustworthy, Responsible AI. Frontiers in Artificial Intelligence, 5(1). https://doi.org/10.3389/frai.2022.779799
Gichoya J. W., Thomas, K. J., Leo Anthony Celi, Safdar, N. M., Banerjee, I., Banja, J. D., Laleh Seyyed-Kalantari, Trivedi, H., & Saptarshi Purkayastha. (2023). AI pitfalls and what not to do: Mitigating bias in AI. British Journal of Radiology, 96(1150). https://doi.org/10.1259/bjr.20230023
Gupta, S., & Gupta, B. (2022). Insights into the Black Box Machine Learning Models Through Explainability and Interpretability. Lecture Notes in Networks and Systems, 633–644. https://doi.org/10.1007/978-981-16-9967-2_59
Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2021). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03856-6
Hassan, A. O., Ewuga, S. K., Abdul, A. A., Abrahams, T. O., Oladeinde, M., & Dawodu, S. O. (2024). CYBERSECURITY IN BANKING: A GLOBAL PERSPECTIVE WITH A FOCUS ON NIGERIAN PRACTICES. Computer Science & IT Research Journal, 5(1), 41–59. https://doi.org/10.51594/csitrj.v5i1.701
Hassija V., Vinay Chamola, Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2023). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16. https://doi.org/10.1007/s12559-023-10179-8
Hilal, W., Andrew Gadsden, S., & Yawney, J. (2021). A Review of Anomaly Detection Techniques and Applications in Financial Fraud. Expert Systems with Applications, 193(1), 116429. https://doi.org/10.1016/j.eswa.2021.116429
Jiang, J., Kahai, S., & Yang, M. (2022). Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty. International Journal of Human-Computer Studies, 165, 102839. https://doi.org/10.1016/j.ijhcs.2022.102839
Koster, O., Kosman, R., & Visser, J. (2021). A Checklist for Explainable AI in the Insurance Domain. Communications in Computer and Information Science, 446–456. https://doi.org/10.1007/978-3-030-85347-1_32
Kumar, J. R. R., Kalnawat, A., Pawar, A. M., Jadhav, V. D., Srilatha, P., & Khetani, V. (2024). Transparency in Algorithmic Decision-making: Interpretable Models for Ethical Accountability. E3S Web of Conferences, 491, 02041. https://doi.org/10.1051/e3sconf/202449102041
Leslie, D. (2019). Understanding artificial intelligence ethics and safety A guide for the responsible design and implementation of AI systems in the public sector Dr David Leslie Public Policy Programme. Understanding Artificial Intelligence Ethics and Safety. https://doi.org/10.5281/zenodo.3240529
Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. Expert Systems with Applications, 210, 118354. https://doi.org/10.1016/j.eswa.2022.118354
Marcinkevičs, R., & Vogt, J. E. (2023). Interpretable and explainable machine learning: A methods‐centric overview with concrete examples. WIREs Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1493
Max, R., Kriebitz, A., & Von Websky, C. (2021). Ethical Considerations About the Implications of Artificial Intelligence in Finance. International Handbooks in Business Ethics, 577–592. https://doi.org/10.1007/978-3-030-29371-0_21
McWaters, R. J. (2019, October 23). Navigating Uncharted Waters: A roadmap to responsible innovation with AI in financial services. World Economic Forum. https://www.weforum.org/publications/navigating-uncharted-waters-a-roadmap-to-responsible-innovation-with-ai-in-financial-services/
Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2020). Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities. Information Systems Management, 39(1), 1–11. https://doi.org/10.1080/10580530.2020.1849465
Messalas, A., Kanellopoulos, Y., & Makris, C. (2019, July 1). Model-Agnostic Interpretability with Shapley Values. IEEE Xplore. https://doi.org/10.1109/IISA.2019.8900669
Miller, T., Howe, P., & Sonenberg, L. (2017, December 4). Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences. ArXiv.org. https://doi.org/10.48550/arXiv.1712.00547
Misheva, B. H., Osterrieder, J., Hirsa, A., Kulkarni, O., & Lin, S. F. (2021). Explainable AI in Credit Risk Management. Arxiv.org. https://doi.org/10.48550/arXiv.2103.00949
Mohanty, B., Manipal, A., & Mishra, S. (2023). ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL FRAUD DETECTION. Academy of Marketing Studies Journal, 27(1). https://www.abacademies.org/articles/role-of-artificial-intelligence-in-financial-fraud-detection.pdf
Mohseni, S., Zarei, N., & Ragan, E. D. (2021). A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems. ACM Transactions on Interactive Intelligent Systems, 11(3-4), 1–45. https://doi.org/10.1145/3387166
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2021). From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices. Philosophical Studies Series, 153–183. https://doi.org/10.1007/978-3-030-81907-1_10
Odeyemi, O., Noluthando Zamanjomane Mhlongo, Ekene Ezinwa Nwankwo, & Oluwatobi Timothy Soyombo. (2024). Reviewing the role of AI in fraud detection and prevention in financial services. International Journal of Science and Research Archive, 11(1), 2101–2110. https://doi.org/10.30574/ijsra.2024.11.1.0279
Oladele I., Orelaja A., & Akinwande O. T. (2024). Ethical Implications and Governance of Artificial Intelligence in Business Decisions: A Deep Dive into the Ethical Challenges and Governance Issues Surrounding the Use of Artificial Intelligence in Making Critical Business Decisions. International Journal of Latest Technology in Engineering Management & Applied Science, XIII(II), 48–56. https://doi.org/10.51583/ijltemas.2024.130207
Pinto, S. O., & Sobreiro, V. A. (2022). Literature review: Anomaly detection approaches on digital business financial systems. Digital Business, 100038. https://doi.org/10.1016/j.digbus.2022.100038
Qadi A. E., Diaz-Rodriguez, N., Trocan, M., & Frossard, T. (2021). Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2103.08359
Rane, N., Choudhary, S., & Rane, J. (2023). Explainable Artificial Intelligence (XAI) approaches for transparency and accountability in financial decision-making. Social Science Research Network. https://doi.org/10.2139/ssrn.4640316
Ribeiro M. T., Singh, S., & Guestrin, C. (2016). Model-Agnostic Interpretability of Machine Learning. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1606.05386
Saeed, W., & Omlin, C. (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems, 263, 110273. https://doi.org/10.1016/j.knosys.2023.110273
Schwalbe, G., & Finzel, B. (2023). A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-022-00867-8
Scott, A. C., Clancey, W. J., Davis, R., & Shortliffe, E. H. (1977). Explanation Capabilities of Production-Based Consultation Systems. American Journal of Computational Linguistics, 1–50. https://aclanthology.org/J77-1006
Shneiderman, B. (2020). Bridging the Gap Between Ethics and Practice. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31. https://dl.acm.org/doi/abs/10.1145/3419764
Swartout, W. R. (1981). Explaining and Justifying Expert Consulting Programs. Computers and Medicine, 254–271. https://doi.org/10.1007/978-1-4612-5108-8_15
Tursunalieva A., David, Dunne, R., Li, J., Riera, L., & Zhao, Y. (2024). Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications. Applied Sciences, 14(2), 496–496. https://doi.org/10.3390/app14020496
Vivian W.-M. Lai, Liu, H., & Tan, C. (2020). “Why is ‘Chicago’ deceptive?” Towards Building Model-Driven Tutorials for Humans. Proc. ACM Hum. - Comput. Interact. 7, CSCW2, Article 357. https://doi.org/10.1145/3313831.3376873
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924.
Wang, W., Jones, P., & Partridge, D. (2000). Assessing the Impact of Input Features in a Feedforward Neural Network. Neural Computing & Applications, 9(2), 101–112. https://doi.org/10.1007/pl00009895
Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. Natural Language Processing and Chinese Computing, 11839, 563–574. https://doi.org/10.1007/978-3-030-32236-6_51
Xu, Y., Wang, Q., An, Z., Wang, F., Zhang, L., Wu, Y., Dong, F., Qiu, C.-W., Liu, X., Qiu, J., Hua, K., Su, W., Xu, H., Han, Y., Cao, X., Liu, E., Fu, C., Yin, Z., Liu, M., & Roepman, R. (2021). Artificial Intelligence: A Powerful Paradigm for Scientific Research. The Innovation, 2(4), 100179. Sciencedirect.
Zhao, J., & Gómez Fariñas, B. (2022). Artificial Intelligence and Sustainable Decisions. European Business Organization Law Review, 24(1). https://doi.org/10.1007/s40804-022-00262-2
Zhou, F., Ayoub, J., Xu, Q., & Jessie Yang, X. (2019). A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems. Journal of Mechanical Design, 142(1). https://doi.org/10.1115/1.4044435
Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021). Intelligent Financial Fraud Detection Practices in Post-Pandemic Era: A Survey. The Innovation, 2(4), 100176. https://doi.org/10.1016/j.xinn.2021.100176