Business Process Improvement in Finance Operations through Predictive Analytics and Decision Intelligence

References

[1] Addy, W. A., Ugochukwu, C. E., Oyewole, A. T., Ofodile, O. C., Adeoye, O. B., & Okoye, C. C. (2024). Predictive analytics in credit risk management for banks: A comprehensive review. GSC Advanced Research and Reviews, 18(2), 434–449. https://doi.org/10.30574/gscarr.2024.18.2.0077

[2] Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., & Abhulimen, A. O. (2024). Integrating business intelligence and predictive analytics in banking: A framework for optimizing financial decision-making. Finance & Accounting Research Journal, 6(8), 1517–1530. https://doi.org/10.51594/farj.v6i8.1505

[3] Al-Okaily, A., Teoh, A. P., & Al-Okaily, M. (2023). Evaluation of data analytics-oriented business intelligence technology effectiveness: An enterprise-level analysis. Business Process Management Journal, 29(3), 777–800. https://doi.org/10.1108/BPMJ-10-2022-0546

[4] Al-Sai, Z. A., Husin, M. H., Syed-Mohamad, S. M., Abdin, R. M. D. S., Damer, N., Abualigah, L., & Gandomi, A. H. (2022). Explore big data analytics applications and opportunities: A review. Big Data and Cognitive Computing, 6(4), 157. https://doi.org/10.3390/bdcc6040157

[5] Baiyere, A., Salmela, H., & Tapanainen, T. (2020). Digital transformation and the new logics of business process management. European Journal of Information Systems, 29(3), 238–259. https://doi.org/10.1080/0960085X.2020.1718007

[6] Bany Mohammad, A., Al-Okaily, M., Al-Majali, M., & Masa'deh, R. (2022). Business intelligence and analytics usage in the banking industry sector: An application of the TOE framework. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 189. https://doi.org/10.3390/joitmc8040189

[7] Bedford, D. S., Derichs, D., Hoozee, S., Malmi, T., Messner, M., Sinha, V. K., Van der Kolk, B., & Verbeeten, F. (2025). Digitalization of the finance function: Automation, analytics, and finance function effectiveness. Management Accounting Research, 67, 100942. https://doi.org/10.1016/j.mar.2025.100942

[8] Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025–1044. https://doi.org/10.1287/mnsc.2018.3253

[9] Cao, L. (2023). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), Article 64, 1–38. https://doi.org/10.1145/3502289

[10] Chakraborty, G. (2020). Evolving profiles of financial risk management in the era of digitization: The tomorrow that began in the past. Journal of Public Affairs, 20(2), e2034. https://doi.org/10.1002/pa.2034

[11] Chen, Y., & Lin, Z. (2021). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, 57, 102232. https://doi.org/10.1016/j.ijinfomgt.2020.102232

[12] Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1–16. https://doi.org/10.1016/j.ejor.2022.04.027

[13] Duan, H. K., Vasarhelyi, M. A., & Codesso, M. (2025). Integrating process mining and machine learning for advanced internal control evaluation in auditing. Journal of Information Systems, 39(1), 55–75. https://doi.org/10.2308/ISYS-2022-028

[14] Fedushko, S., Ustyianovych, T., & Gregus, M. (2020). Real-time high-load infrastructure transaction status output prediction using operational intelligence and big data technologies. Electronics, 9(4), 668. https://doi.org/10.3390/electronics9040668

[15] Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial risk management and explainable, trustworthy, responsible AI. Frontiers in Artificial Intelligence, 5, 779799. https://doi.org/10.3389/frai.2022.779799

[16] Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577

[17] Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020). Digital analytics: Modeling for insights and new methods. Journal of Interactive Marketing, 51, 26–43. https://doi.org/10.1016/j.intmar.2020.04.003

[18] Knudsen, D. R. (2020). Elusive boundaries, power relations, and knowledge production: A systematic review of the literature on digitalization in accounting. International Journal of Accounting Information Systems, 36, 100441. https://doi.org/10.1016/j.accinf.2019.100441

[19] Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003

[20] Maja, M. M., & Letaba, P. (2022). Towards a data-driven technology roadmap for the bank of the future: Exploring big data analytics to support technology roadmapping. Social Sciences & Humanities Open, 6(1), 100270. https://doi.org/10.1016/j.ssaho.2022.100270

[21] Mohammed, A. B., Al-Okaily, M., Qasim, D., & Al-Majali, M. K. (2024). Towards an understanding of business intelligence and analytics usage: Evidence from the banking industry. International Journal of Information Management Data Insights, 4(1), 100215. https://doi.org/10.1016/j.jjimei.2024.100215

[22] Obeng, S., Iyelolu, T. V., Akinsulire, A. A., & Idemudia, C. (2024). Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security. World Journal of Advanced Research and Reviews, 23(1), 1972–1980. https://doi.org/10.30574/wjarr.2024.23.1.2185

[23] Patel, K. (2023). Credit card analytics: A review of fraud detection and risk assessment techniques. International Journal of Computer Trends and Technology, 71(10), 69–79. https://doi.org/10.14445/22312803/IJCTT-V71I10P109

[24] Rangineni, S., Bhanushali, A., Suryadevara, M., Venkata, S., & Peddireddy, K. (2023). A review on enhancing data quality for optimal data analytics performance. International Journal of Computer Sciences and Engineering, 11(10), 51–58. https://doi.org/10.26438/ijcse/v11i10.5158

[25] Santos, M. Y., & Costa, C. (2020). Big data: Concepts, warehousing, and analytics. River Publishers.

[26] Singh, V., Chen, S. S., Singhania, M., Nanavati, B., & Gupta, A. (2022). How are reinforcement learning and deep learning algorithms used for big data-based decision making in financial industries? A review and research agenda. International Journal of Information Management Data Insights, 2(2), 100094. https://doi.org/10.1016/j.jjimei.2022.100094

[27] Soltani Delgosha, M., Hajiheydari, N., & Fahimi, S. M. (2021). Elucidation of big data analytics in banking: A four-stage Delphi study. Journal of Enterprise Information Management, 34(6), 1577–1596. https://doi.org/10.1108/JEIM-03-2019-0097

[28] Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J. J., Ouyang, C., ter Hofstede, A. H. M., van de Weerd, I., Wynn, M. T., & Reijers, H. A. (2020). Robotic process automation: Contemporary themes and challenges. Computers in Industry, 115, 103162. https://doi.org/10.1016/j.compind.2019.103162

[29] Werner, M., Wiese, M., & Maas, A. (2021). Embedding process mining into financial statement audits. International Journal of Accounting Information Systems, 41, 100514. https://doi.org/10.1016/j.accinf.2021.100514

[30] Zhang, H., Zhao, S., Kou, G., Li, C. C., Dong, Y., & Herrera, F. (2020). An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Information Fusion, 60, 65–79. https://doi.org/10.1016/j.inffus.2020.03.001

@article{1722320,
      author = {Muhammad Zeeshan Qureshi},
      title = {Business Process Improvement in Finance Operations through Predictive Analytics and Decision Intelligence},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {1424-1436},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722320.pdf},
      abstract = {Finance operations are increasingly required to accelerate closing cycles, improve forecast accuracy, strengthen controls, and provide decision support without increasing administrative costs. Predictive analytics enables the anticipation of cash shortfalls, late payments, atypical transactions, workload peaks, and close delays. However, predictions alone do not improve processes unless they are translated into governed actions. This review analyses how predictive analytics and decision intelligence jointly enable business process improvement across procure-to-pay, order-to-cash, record-to-report, financial planning and analysis, treasury, and compliance. A structured integrative review of thirty publications from 2020 to 2025 synthesises research on business intelligence, process management, machine learning, process mining, automation, and responsible financial analytics. The analysis identifies four mechanisms of improvement: earlier exception detection, dynamic prioritisation, resource and working-capital optimisation, and closed-loop learning from decision outcomes. It further finds that value creation relies more on data quality, workflow integration, explainability, accountability, and feedback design than on model sophistication. This paper presents a Finance Decision Intelligence Improvement Framework that links event data, predictive systems, decision rules, human judgement, automated execution, and performance monitoring. The framework distinguishes prediction quality from decision quality and process value, thereby reducing the risk of technically accurate models that fail operationally. The review concludes that predictive analytics achieves sustainable finance improvement when integrated as a controlled decision service rather than as a stand-alone dashboard.},
      keywords = {finance operations, business process improvement, predictive analytics, decision intelligence, process mining, financial transformation},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722320}
  }