Kecerdasan Buatan dalam Sistem Pendukung Keputusan Keuangan: Tinjauan Literatur Sistematis (2020–2026)
DOI:
https://doi.org/10.61132/pajamkeu.v3i3.2300Keywords:
Artificial Intelligenc, Decision Making, Finance, Prisma, Systematic Literature ReviewAbstract
The growing development of Artificial Intelligence (AI) research in the financial industry reflects the increasing integration of advanced technologies into financial activities and services. This growth has prompted scholars to explore which areas of finance are most frequently studied and what types of AI applications are most common in current academic discussions. To address these questions, this study uses a systematic literature review (SLR) by examining journal articles indexed in Scopus, from Q1 to Q4 classifications, published between 2020 and 2026. The initial search identified 907 relevant publications. However, after applying the PRISMA screening and eligibility procedures, only 31 articles met the inclusion criteria and were selected for further review. These studies were analyzed using bibliometric mapping with VOSviewer software and supplemented by qualitative content analysis. The results show that Fintech and risk management are the most widely discussed topics in AI-related financial research. Additionally, Machine Learning is the most prominent AI technology used in the financial sector, especially in fostering innovation, enhancing operational performance, and supporting strategic decision-making. The review also emphasizes that combining Machine Learning and risk management is the most important and rapidly growing area of research within finance.
Downloads
References
Alassuli, A., Eltweri, A., Thuneibat, N. S., Al-Hajaya, K., & Ismail, S. M. (2026). Artificial intelligence applications and financial forecasting accuracy in banking platforms: Evidence from Jordan. Administrative Sciences, 16(3). https://doi.org/10.3390/admsci16030122
Al-Hunaiti, M. A., Khrais, L. T., Ali, H., Alkhodary, D., Haikal, E. K., & Morshed, A. (2025). Impact of advanced technologies on supply chain management: Legal challenges and integration strategies. Corporate and Business Strategy Review, 6(2), 62–70. https://doi.org/10.22495/cbsrv6i2art6
Aya, L. T. P., Polo, O. C. C., Escobar, S. B. V., Saldaña, O. T., Tarrillo, L. A. B., Morales, M. E. L., Castillo, L. R., Araujo, P. A. V., & Rosas, C. G. (2025). The role of AI: From conventional methods to digital crime analysis. International Journal of Accounting and Economics Studies, 12(5), 1201–1206. https://doi.org/10.14419/ap57rx96
Chen, Y., Tian, S., & Li, H. (2025). Integrating AI and process reengineering in financial shared services: A multi-stage implementation framework. International Journal of Information Systems in the Service Sector, 16(1). https://doi.org/10.4018/IJISSS.396822
Chen, Z. (2022). An online-decision algorithm for the multi-period bank clearing problem. Journal of Industrial and Management Optimization, 18(4), 2783–2803. https://doi.org/10.3934/jimo.2021091
Cornwell, N., Bilson, C., Gepp, A., Stern, S., & Vanstone, B. J. (2023). The role of data analytics within operational risk management: A systematic review from the financial services and energy sectors. Journal of the Operational Research Society, 74(1), 374–402. https://doi.org/10.1080/01605682.2022.2041373
Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62. https://doi.org/10.1016/j.techsoc.2020.101257
Darmon, E., Oriol, N., & Rufini, A. (2022). Bids for speed: An empirical study of investment strategy automation in a peer-to-business lending platform. Decision Support Systems, 156. https://doi.org/10.1016/j.dss.2022.113732
De Caigny, A., Coussement, K., & De Bock, K. W. (2020). Leveraging fine-grained transaction data for customer life event predictions. Decision Support Systems, 130. https://doi.org/10.1016/j.dss.2019.113232
Du, Y., Li, B., Lu, Z., & Kou, G. (2025). Modeling hybrid firm relationships with graph neural networks for stock investment decisions. Decision Support Systems, 198. https://doi.org/10.1016/j.dss.2025.114528
Duan, W., Hu, N., & Xue, F. (2024). The information content of financial statement fraud risk: An ensemble learning approach. Decision Support Systems, 182. https://doi.org/10.1016/j.dss.2024.114231
Duckworth, C., Zlatev, Z., Sciberras, J., Hallett, P., & Gerding, E. (2025). Optimising task allocation to balance business goals and worker well-being for financial service workforces. Journal of Modelling in Management, 20(5), 1515–1536. https://doi.org/10.1108/JM2-11-2023-0263
Karoui, C. (2026). AI-enabled hybrid ensemble learning for imbalanced credit risk prediction: A human-in-the-loop decision support framework. Journal of Project Management (Canada), 11(2), 449–456. https://doi.org/10.5267/j.jpm.2026.2.003
Kartanaitė, I., Kovalov, B., Kubatko, O., & Krušinskas, R. (2021). Financial modeling trends for production companies in the context of Industry 4.0. Investment Management and Financial Innovations, 18(1), 270–284. https://doi.org/10.21511/imfi.18(1).2021.23
Kim, D., Kang, S., & Hong, A. (2026). Bridging the maturity-expectation gap: Generative AI in strategic decision-making for public R&D interim review. Technovation, 149. https://doi.org/10.1016/j.technovation.2025.103374
Kumkum, S., Tapan Mahmud, M., Adnan, A., & Hasan, M. K. (2026). Impact of artificial intelligence on decision-making quality in mobile financial services in Bangladesh: The mediating role of risk mitigation. Journal of Decision Systems, 35(1). https://doi.org/10.1080/12460125.2026.2620371
Leppinen, J., Salo, A., & Compare, M. (2026). A stage-gate decision process for guiding the development of AI solutions for preventive maintenance. EURO Journal on Decision Processes, 14. https://doi.org/10.1016/j.ejdp.2025.100063
Li, B. (2025). The impact and role analysis of artificial intelligence technology on the development of the accounting industry. International Journal of Knowledge Management, 21(1). https://doi.org/10.4018/IJKM.370950
Nevi, G., Palazzo, M., Ferri, M. A., & Dezi, L. (2025). Redefining identity: Corporate evolution in the AI era. European Journal of Innovation Management, 1–33. https://doi.org/10.1108/EJIM-03-2025-0389
Pandey, S. K., & Ahmed, M. (2025). Strategic management practices for AI-enabled financial planning in technology-intensive manufacturing firms. Archives for Technical Sciences, 17(34), 316–323. https://doi.org/10.70102/afts.2025.1834.316
Psarommatis, F., Danishvar, M., Mousavi, A., & Kiritsis, D. (2024). Cost-based decision support system: A dynamic cost estimation of key performance indicators in manufacturing. IEEE Transactions on Engineering Management, 71, 702–714. https://doi.org/10.1109/TEM.2021.3133619
Shapovalova, A., Kuzmenko, O., Polishchuk, O., Larikova, T., & Myronchuk, Z. (2023). Modernization of the national accounting and auditing system using digital transformation tools. Financial and Credit Activity: Problems of Theory and Practice, 4(51), 33–52. https://doi.org/10.55643/fcaptp.4.51.2023.4102
Strunk, J., Nissen, A., & Smolnik, S. (2025). All risks ain't the same: A risk facets perspective on AI-based decision support systems. Decision Support Systems, 199. https://doi.org/10.1016/j.dss.2025.114557
Trincanato, E., & Vagnoni, E. (2024). Business intelligence and the leverage of information in healthcare organizations from a managerial perspective: A systematic literature review and research agenda. Journal of Health Organization and Management, 38(3), 305–330. https://doi.org/10.1108/JHOM-02-2023-0039
Wang, S. (2026). Harnessing AI-enhanced financial statement analytics for intelligent resource management: A closed-loop framework. Information Resources Management Journal, 39(1). https://doi.org/10.4018/IRMJ.399504
Wang, Y., Zhang, X., & Yin, Y. (2026). Artificial intelligence-driven financial fraud identification in informatics-enabled service systems. Journal of Logistics, Informatics and Service Science, 13(3), 176–194. https://doi.org/10.33168/JLISS.2026.0309
Yang, S. (2026). Artificial intelligence applications in intelligent financial decision support systems: Integrating big data and service science for supply chain finance optimization. Journal of Logistics, Informatics and Service Science, 13(2), 246–266. https://doi.org/10.33168/JLISS.2026.0214
Yang, T., Yu, T. R., & Zhao, H. (2024). Uncovering the relationship between incidental emotion toward a disaster and stock market fluctuations: Evidence from the US market. Decision Support Systems, 181. https://doi.org/10.1016/j.dss.2024.114213
Yuan, J. (2025). A big data-driven information model for enterprise financial risk management: Model development and empirical validation. Information Resources Management Journal, 38(1). https://doi.org/10.4018/IRMJ.396698
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Pajak dan Manajemen Keuangan

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




