Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
89
Citations
146.5k
Views
53
Articles
Volume 4 Number 1 2026
RESEARCH ARTICLE (Open Access)
Explainable AI in Automated Financial Reconciliation: A Survey-Based Analysis Across Multi-Location Enterprises
Nasir Uddin 1*
Business and Social Sciences 4 (1) 1-8 https://doi.org/10.25163/business.4110905
Submitted: 20 September 2026 Revised: 27 October 2026 Accepted: 04 November 2026 Published: 06 November 2026
Abstract
Background: Multi-location enterprises increasingly rely on artificial intelligence to reconcile financial transactions across branches, banking systems, and enterprise resource planning platforms, yet the opacity of many AI models continues to undermine the confidence auditors, controllers, and regulators place in automated outputs (Cerneviciene & Kabašinskas, 2024). Explainable AI (XAI) has been proposed as a bridge between automation and accountability, though empirical evidence on its organizational value in reconciliation contexts specifically — as opposed to credit scoring or fraud detection, where most XAI-finance research has concentrated — remains limited.
Methods: A cross-sectional, structured online survey was administered to 175 professionals working in finance, accounting, auditing, information technology, data analytics, and organizational management. Seven constructs — Explainable AI Capability, AI Explainability and Transparency, Automated Reconciliation Quality, Exception Detection Capability, Multi-Location Consistency, Financial Control Effectiveness, and Audit and Decision Support — were measured on five-point Likert scales and analyzed descriptively, via Pearson correlation, and via exploratory factor analysis (principal component extraction).
Results: All constructs were rated favorably (means 4.09–4.24). Automated Reconciliation Quality was rated highest (M = 4.24); Multi-Location Consistency was rated lowest (M = 4.09). Automation Quality correlated most strongly with Reconciliation Effectiveness (r = .748), followed by XAI Capability (r = .703). Five factors explained 86.90% of total variance.
Conclusion: Respondents perceive explainability as a meaningful contributor to reconciliation quality, financial control, and decision support, though cross-location consistency and the clarity of AI-generated explanations remain comparatively weaker areas that warrant targeted investment.
Keywords: Explainable Artificial Intelligence; Financial Reconciliation; Multi-Location Enterprises; Exception Detection; Financial Control
References
Eysenbach, G. (2004). Improving the quality of Web surveys: The Checklist for Reporting Results of Internet E-Surveys (CHERRIES). Journal of Medical Internet Research, 6(3), e34. https://doi.org/10.2196/jmir.6.3.e34
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
Klumpp, M., Hintze, M., Immonen, M., Ródenas-Rigla, F., Pilati, F., Aparicio-Martínez, F., Çelebi, D., Liebig, T., Jirstrand, M., Urbann, O., Hedman, M., Lipponen, J. A., Bicciato, S., Radan, A., Valdivieso, B., Thronicke, W., Gunopulos, D., & Delgado-Gonzalo, R. (2021). Artificial intelligence for hospital health care: Application cases and answers to challenges in European hospitals. Healthcare, 9(8), 961. https://doi.org/10.3390/healthcare9080961
Kokina, J., & Blanchette, S. (2019). Early evidence of digital labor in accounting: Innovation with Robotic Process Automation. International Journal of Accounting Information Systems, 35, 100431. https://doi.org/10.1016/j.accinf.2019.100431
Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 15(1), 21–35.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Cerneviciene, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57(8), Article 216. https://doi.org/10.1007/s10462-024-10854-8
Cerneviciene, J., & Kabašinskas, A. (2022). Review of multi-criteria decision-making methods in finance using explainable artificial intelligence. Frontiers in Artificial Intelligence, 5, 827584. https://doi.org/10.3389/frai.2022.827584
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. The Lancet, 370(9596), 1453–1457. https://doi.org/10.1016/S0140-6736(07)61602-X
Yang, M., Xu, C., Bai, Y., Ma, M., & Su, X. (2025). Investigating black-box model for wind power forecasting using Local Interpretable Model-Agnostic Explanations algorithm. CSEE Journal of Power and Energy Systems. https://doi.org/10.17775/CSEEJPES.2021.07470
Recommended articles
Explainable AI Framework for Detecting and Reducing Health Disparities in Healthcare Supply Chains
Article metrics
View details
0
Downloads
0
Citations
76
Views
0
Save
Save
0
Citation
Citation
76
View
View
0
Share
Share