Examining the Role of Financial Digital Twin in Economic Scenario Simulation, Risk Management, and Strategic Decision-Making in Companies
Keywords:
Financial digital twin, Scenario simulation, Risk management, Strategic decision-making, Predictive analytics, Data governance.Abstract
This study aimed to explain the role of financial digital twin in economic scenario simulation, risk management enhancement, and strategic decision-making support from the perspective of financial, risk, strategy, and information technology experts in Tehran. This qualitative study was conducted using thematic analysis. The participants consisted of 18 senior financial managers, risk managers, strategy managers, senior financial analysts, and data and information technology specialists working in companies located in Tehran. They were selected through purposive sampling with maximum variation. Data were collected exclusively through semi-structured interviews. Interviews continued until theoretical saturation was achieved; saturation occurred at the sixteenth interview, and two additional interviews were conducted to confirm data adequacy. Data analysis was performed through open, axial, and selective coding using NVivo software. To enhance the trustworthiness of the findings, member checking, peer debriefing, audit trail, constant comparison, and researcher reflexivity were applied. The analysis revealed five main categories explaining the role of financial digital twin: “data integration and intelligent financial architecture,” “economic scenario simulation and outcome prediction,” “dynamic risk management and early warning,” “strategic decision support and resource allocation,” and “data governance, model trustworthiness, and organizational readiness.” The findings indicated that financial digital twins can integrate financial, operational, and environmental data to create a dynamic representation of the company’s financial condition. This enables firms to test strategic decisions in a virtual environment before implementing them in real operations. Participants emphasized that the value of financial digital twins depends on data quality, model interpretability, senior management commitment, and integration with enterprise risk management systems. Financial digital twin can be considered a strategic infrastructure for moving from reactive and backward-looking financial decision-making toward predictive, scenario-based, and data-driven decision-making. However, successful implementation requires data maturity, information system integration, model governance, algorithmic transparency, and the development of analytical capabilities among financial and strategic managers.
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References
Anshari, M., Almunawar, M. N., Masri, M., & Hamdan, M. (2022). Digital twin: Financial technology’s next frontier of robo-advisor. Journal of Risk and Financial Management, 15(4), 163. https://doi.org/10.3390/jrfm15040163
Attaran, M., & Celik, B. G. (2023). Digital twin: Benefits, use cases, challenges, and opportunities. Decision Analytics Journal, 6, 100165. https://doi.org/10.1016/j.dajour.2023.100165
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
COSO. (2017). Enterprise risk management—Integrating with strategy and performance. Committee of Sponsoring Organizations of the Treadway Commission.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage.
Fornari, F., Compagnucci, I., De Donato, M. C., Bertrand, Y., Beyel, H. H., Carrión, E., Franceschetti, M., Groher, W., Grüger, J., Kilic, E., Koschmider, A., Leotta, F., Li, C.-Y., Lugaresi, G., Malburg, L., Mangler, J., Mecella, M., Pastor, O., Riss, U., Seiger, R., & Valderas, P. (2025). Digital twins of business processes: A research manifesto. Internet of Things, 30, 101477. https://doi.org/10.1016/j.iot.2024.101477
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
Hennink, M. M., Kaiser, B. N., & Marconi, V. C. (2017). Code saturation versus meaning saturation: How many interviews are enough? Qualitative Health Research, 27(4), 591–608. https://doi.org/10.1177/1049732316665344
International Organization for Standardization. (2018). ISO 31000:2018 Risk management—Guidelines. ISO.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Passas, I. (2025). From digital twins to digital triplets in economics and financial decision-making. Encyclopedia, 5(3), 87. https://doi.org/10.3390/encyclopedia5030087
Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in Industry, 130, 103469. https://doi.org/10.1016/j.compind.2021.103469
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Wei, Y. (2025). A digital twin model for grain enterprise financial shared service centers based on distributed deep learning and neural symbolic reasoning. Scientific Reports, 15, Article 24350. https://doi.org/10.1038/s41598-025-24350-8
