Designing an Optimal Model for an Intelligent Decision Support System in Banking Organizations Using a Financial Intelligence Approach
Keywords:
Decision support system, financial intelligence, banking systemAbstract
This study aimed to design an optimal and context-specific model for an intelligent decision support system in banking organizations with an emphasis on financial intelligence. This research adopted an applied, exploratory, and qualitative design based on an interpretive paradigm. The study participants consisted of academic experts and senior banking managers familiar with decision support systems, who were selected through purposive snowball sampling. Data were collected via semi-structured interviews and continued until theoretical saturation was achieved, resulting in ten interviews for final analysis. The collected data were analyzed using thematic analysis with the assistance of MAXQDA software. The credibility and trustworthiness of the findings were ensured using Lincoln and Guba’s criteria, and reliability was confirmed through inter-coder agreement. The analysis yielded 351 initial codes, which were synthesized into 15 organizing themes and ultimately condensed into six overarching themes, including primary database infrastructure, decision support database, information security and protection, knowledge management, financial intelligence orientation, and command processing unit. These themes were integrated into a coherent conceptual model illustrating the structural relationships among the core components of an intelligent decision support system in banking organizations. The proposed model demonstrates that integrating financial intelligence with robust data infrastructures, information security mechanisms, and knowledge management processes can significantly enhance managerial decision-making effectiveness and overall organizational performance in the banking sector.
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Aghaei, G. (2019). Analysis of the Relationship between Banking Intelligence Components and Employee Creativity at Khuzestan Regional Electric Company Bank. The Fifth International Conference on Management and Accounting Sciences, Tehran.
Ali, M. R., Nipu, S. M. A., & Khan, S. A. (2023). A decision support system for classifying supplier selection criteria using machine learning and random forest approach. Decision Analytics Journal, 7, 100238.
Balicka, H. (2023). Digital technologies in the accounting information system supporting decision-making processes. Scientific Papers of Silesian University of Technology-Organization and Management Series(169). https://doi.org/10.29119/1641-3466.2023.169.4
Bazadeh, S., Abbasi, E., Didehkhani, H., & Khozean, A. (2021). Designing a Smart Model for Predicting Financial Solvency in Insurance Companies (A Data Mining Approach). Investment Knowledge, 9(34), 211-229. http://www.jik-ifea.ir/article_16206.html
Binazir, A., Mehdi, A., & Hassan, A. (2018). Modeling and Managing Bank Human Resource Performance by Utilizing a Hybrid Model of Balanced Scorecard and Bank Excellence Model (Case Study: Iran Khodro Sales Bank). The Fifth National Conference on Applied Research in Management and Accounting, Tehran.
Boisjoly, R. P., Conine, T. E., & McDonald, M. B. (2020). Working capital management: Financial and valuation impacts. Journal of Business Research, 108, 1-8. https://doi.org/10.1016/j.jbusres.2019.09.025
Bouzguenda, K. (2018). Emotional intelligence and financial decision making: Are we talking about a paradigmatic shift or a change in practices? Research in International Business and Finance, 44, 273-284. https://doi.org/10.1016/j.ribaf.2017.07.096
Chipriyanova, G., & Krasteva-Hristova, R. (2023). Technological Aspects of Accounting Automation System as a Decision Support System. Environment Technology Resources Proceedings of the International Scientific and Practical Conference, 2, 28-33. https://doi.org/10.17770/etr2023vol2.7309
De Sousa, W. G., De Melo, E. R. P., Bermejo, P. H. D. S., Farias, R. A. S., & Gomes, A. O. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392. https://doi.org/10.1016/j.giq.2019.07.004
Desouza, K. C., Dawson, G. S., & Chenok, D. (2019). Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons. https://www.sciencedirect.com/science/article/abs/pii/S0007681319301582
Domínguez, E., Pérez, B., Rubio, A. L., & Zapata, M. A. (2019). A taxonomy for key performance indicators management. Computer Standards & Interfaces, 64, 24-40. https://doi.org/10.1016/j.csi.2018.12.001
Ehtesham Mehr, H., Kamiabi, Y., & Khalilpour, M. (2021). The Relationship between Smart Business and Integrated Reporting and Its Impact on the Financial Performance of Companies Listed on the Tehran Stock Exchange. Financial Economics, 15(57), 333-358. https://sid.ir/paper/416561/en
Ghasemi, M., Alireza, P., & Hamid Mahdavi, R. (2018). Investigating the Role of Employees' Financial Intelligence on Customer Satisfaction at Mehr Iran Qarz-al-Hasaneh Bank, Hamadan Province. The Second National Conference on Accounting-Management and Economics with an Approach to Sustainable Employment and its Role in Industry Growth, Malayer.
Gupta, S., & Jaiswal, R. (2025). How Can We Improve the Insurance Industry Decision-Support System: Integrating Predictive Analytics and Behavioral Insights Through the INSPIRE Framework. Information Discovery and Delivery. https://doi.org/10.1108/idd-09-2024-0142
Jagan Mohan, R. K., Neelakanteswara, R. A., & Krishnanand, L. (2019). A review on supply chain performance measurement systems. Procedia Manufacturing, 30, 40-47. https://doi.org/10.1016/j.promfg.2019.02.007
Kashkooli, D., & Eskandar, S. (2019). A Comparative Study of the Level of Financial Intelligence and Business Intelligence among Managers with Extroverted and Introverted Personality Types (Case Study: Mehr Eghtesad Bank of East Azerbaijan Province). The Second International Conference on Management, Industrial Engineering, Economics and Accounting, Tbilisi-Georgia.
Kiyosaki, R. (2018). Increase Your Financial Intelligence. Noavvar. https://books.google.to/books?id=M_KKPwAACAAJ&source=gbs_book_other_versions_r&cad=1
Lestari, W. P., Putri, P. H., & Wardani, S. (2023). Decision Support System for Determining Character Education Values. Journal of Intelligent Software Systems, 2(2), 16. https://doi.org/10.26798/jiss.v2i2.1142
Mohammadi, M., & Mehdi Nikbakht, G. (2018). The Role of Staff's Competitive Intelligence, Business Intelligence, and Financial Intelligence in Customer Relationship Management at Qavamin Bank (Case Study: Qavamin Bank of Isfahan Province). The Fourth International Conference on New Horizons in Humanities and Management, Tehran.
Mousavi, S. M. (2018). Performance Management of Bank Branch Human Resources with a Balanced Scorecard and Analytic Network Process Approach (Case Study: Bank Melli Branches in Chalus County). The Seventh National Conference on Applications of Accounting and Management, Tehran.
Nabizadeh Kivi, S., & Maryam, G. (2018). A Study of the Strengths and Weaknesses of the Balanced Scorecard Model in Bank Human Resource Performance Management. The Second National Conference on Modern Research in Accounting and Management in the Third Millennium, Karaj.
Rahimi, H., & Sahar, H. (2018). Investigating the Relationship between Financial Intelligence and Risk-Taking among Isfahan Municipality Employees with a Structural Equation Modeling Approach. The First National Conference on Management and Economics with a Resistance Economy Approach, Mashhad.
Shulajkovska, M., Smerkol, M., Noveski, G., Bohanec, M., & Gams, M. (2024). Artificial Intelligence-Based Decision Support System for Sustainable Urban Mobility. Electronics, 13(18), 3655. https://doi.org/10.3390/electronics13183655
Tortorella, G. L., Miorando, R., & Marodin, G. (2017). Lean supply chain management: Empirical research on practices, contexts and performance. International Journal of Production Economics, 193, 98-112. https://doi.org/10.1016/j.ijpe.2017.07.006
Xu, Z. (2024). Decision Support System for Optimizing Tactics and Strategies of Sports Competition Using Reinforcement Learning Algorithm. Jes, 20(3s), 384-400. https://doi.org/10.52783/jes.1304
Yang, Z. (2023). Design of Intelligent Decision Support System Based on Artificial Intelligence. https://doi.org/10.1117/12.2683893
Yousefli, A., Nourouzi, R., & Hamzeh Yian, A. H. (2023). A rule-based decision support system for the redundancy allocation problem. Industrial Management Studies, 20(65), 85-112. https://doi.org/10.22054/jims.2022.62894.2690
Yusefli, A., Norouzi, R., & Hamzeh Yiyan, A. H. (2023). A Stochastic Rule-Based Decision Support System for Reliability Redundancy Allocation Problem. Industrial Management Studies, 20(65), 85-112. https://www.sid.ir/paper/1033281/en
Zhang, X., Zhang, Y., Liu, X., & Wang, R. (2025). Blockchain-Based Intelligent Risk Management Decision Support System for Supply Chain Financing. International Journal of Intelligent Information Technologies (IJIIT), 21(1), 1-24. https://doi.org/10.4018/IJIIT.369153
Zhu, C. J., & Malcolm, W. (2019). The emergence of Human Resource Management in China: Convergence, divergence and contextualization. Human Resource Management Review, 29(1), 87-97. https://doi.org/10.1016/j.hrmr.2017.11.002
Zohreh, H. E. F. J. (2021). Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment.
