Credit Card Fraud Detection Through Explainable Artificial Intelligence for Managerial Oversight

Authors

  • Muksalmina Muksalmina Department of Management, Faculty of Social Sciences and Education, Universitas Ubudiyah Indonesia, Banda Aceh 23114, Indonesia
  • Ahmad Syahyana Department of Management, Faculty of Social Sciences and Education, Universitas Ubudiyah Indonesia, Banda Aceh 23114, Indonesia
  • Ferdy Hidayatullah Department of Management, Faculty of Social Sciences and Education, Universitas Ubudiyah Indonesia, Banda Aceh 23114, Indonesia
  • Ghalieb Mutig Idroes Interdisciplinary Innovation Research Unit, Graha Primera Saintifika, Aceh Besar 23371, Indonesia
  • Teuku Rizky Noviandy Department of Information Systems, Faculty of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia

DOI:

https://doi.org/10.60084/ijma.v3i1.301

Keywords:

Transparency, Accountability, Compliance, Interpretability, Decision support

Abstract

As digital payment systems grow in volume and complexity, credit card fraud continues to be a significant threat to financial institutions. While machine learning (ML) has emerged as a powerful tool for detecting fraudulent activity, its adoption in managerial settings is hindered by a lack of transparency and interpretability. This study examines how explainable artificial intelligence (XAI) can enhance managerial oversight in the deployment of ML based fraud detection systems. Using a publicly available, simulated dataset of credit card transactions, we developed and evaluated four ML models: Logistic Regression, Naïve Bayes, Decision Tree, and Random Forest. Performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score. The Random Forest model demonstrated superior classification performance but also presented significant interpretability challenges due to its complexity. To fill this gap, we applied SHAP (SHapley Additive exPlanations), a leading method for explaining the outputs of the Random Forest model. SHAP analysis revealed that transaction amount and merchant category were the most influential features in determining the risk of fraud. SHAP plots were used to make these insights accessible to non-technical stakeholders. The findings underscore the importance of XAI in promoting transparency, facilitating regulatory compliance, and fostering trust in AI-driven decisions. This study offers practical guidance for managers, auditors, and policymakers seeking to integrate explainable ML tools into financial risk management processes, ensuring that technological advancements are balanced with accountability and informed human oversight.

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Published

2025-06-08

How to Cite

Muksalmina, M., Syahyana, A., Hidayatullah, F., Idroes, G. M., & Noviandy, T. R. (2025). Credit Card Fraud Detection Through Explainable Artificial Intelligence for Managerial Oversight. Indatu Journal of Management and Accounting, 3(1), 17–28. https://doi.org/10.60084/ijma.v3i1.301

Issue

Vol. 3 No. 1 (2025): June 2025

Section

Articles

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Copyright (c) 2025 Muksalmina Muksalmina, Ahmad Syahyana, Ferdy Hidayatullah, Ghalieb Mutig Idroes, Teuku Rizky Noviandy

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