From Risk Scores to Decision Value: An Evidence-Governed Business Analytics Framework Under Capacity Constraints
Article Sidebar
Main Article Content
Abstract
Organizations increasingly use machine learning to identify customers, transactions, suppliers, and projects that require attention. Yet a high predicted risk does not establish that an intervention will help, that its benefit exceeds its cost, or that the underlying evidence permits action. This paper develops an analytical framework that connects prediction, intervention value, evidence verification, and limited managerial capacity. The framework combines temporally governed data, calibrated risk estimates, bounded intervention effects, constrained optimization, and selective human review. Its central object is a decision record linking each proposed action to the information available at the decision time, the assumptions behind its expected value, and the authority required for execution. Analytical propositions establish when risk ranking agrees with value ranking, how uncertainty changes the intervention threshold, and when additional review has positive expected value. A fully specified six-case worked example demonstrates the distinction. With capacity for two interventions, risk ranking produces an assumed expected net value of −1 unit, whereas value-based selection produces 54 units. A common uncertainty allowance of 0.10 reduces the optimized lower-bound value to 34 units. These quantities are arithmetic consequences of stated assumptions, not estimates from an organizational experiment. The contribution is an auditable decision formulation and a prospective evaluation protocol that separates predictive performance from intervention effectiveness. The framework offers testable design principles for business analytics while identifying the causal, temporal, and organizational evidence required before deployment.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] P. Chakraborty et al., “Leveraging Artificial Intelligence and Machine Learning for Decision-Making in Business Management: A Comprehensive Analysis,” J. Manage., vol. 2, pp. 46–56, 2025, doi: https://doi.org/10.53935/jomw.v2024i4.867.
[2] N. Das et al., “The strategic impact of business intelligence tools: A review of decision-making and ambidexterity,” Membr. Technol., vol. 2025, no. 1, pp. 542–553, 2025, doi: https://doi.org/10.52710/mt.307.
[3] F. Karim et al., “Calibrated, cost-sensitive, explainable ensemble learning for customer churn prediction in retail banking: A leakage-safe pipeline and single-dataset case study,” Front. Artif. Intell., vol. 9, 2026, doi: https://doi.org/10.3389/frai.2026.1889639.
[4] I. Zerine et al., “Explainable churn prediction in telecom with tabular ML five model benchmark and SHAP analysis,” Discov. Artif. Intell., vol. 6, no. 1, 2026, doi: https://doi.org/10.1007/s44163-026-00983-0.
[5] M. S. A. Sourav et al., “Grounding language models with deterministic verifiers for fraud detection,” Discov. Artif. Intell., vol. 6, no. 1, 2026, doi: https://doi.org/10.1007/s44163-026-02355-0.
[6] U. Haldar et al., “The role of artificial intelligence in enhancing customer experience in business,” J. Comput. Anal. Appl., vol. 33, no. 8, pp. 7138–7155, 2024, doi: https://eudoxuspress.com/index.php/pub/article/view/4068.
[7] M. J. Hossain, S. S. Utsab, S. A. Jarir, E. Rozario, Z. A. Tanni, and T. Roy, “Business analytics for United States retail channel planning under structural change,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 3, no. 1, pp. 211–223, 2025, doi: https://doi.org/10.58812/esiscs.v3i01.1263.
[8] N. Prajapati, “Leveraging artificial intelligence to optimize pricing strategies in the refurbished handheld device industry: An engineering perspective,” 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON). IEEE, pp. 1–7, 2025. doi: https://doi.org/10.1109/I2ITCON65200.2025.11210682.
[9] U. Haldar et al., “AI-Driven Business Analytics for Economic Growth Leveraging Machine Learning and MIS for Data-Driven Decision-Making in the U.S. Economy,” J. Posthumanism, vol. 5, no. 4 SE-, pp. 932–957, Apr. 2025, doi: https://doi.org/10.63332/joph.v5i4.1178.
[10] S. Hossain et al., “From Data to Value: Leveraging Business Analytics for Sustainable Management Practices,” J. Posthumanism, vol. 5, no. 5 SE-, pp. 82–105, Apr. 2025, doi: https://doi.org/10.63332/joph.v5i5.1309.
[11] M. M. R. Bhuiyan et al., “Economic implications of homelessness in the U.S. applying advanced business analytics to forecast costs and develop sustainable solutions,” 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, pp. 1–5, 2025. doi: https://doi.org/10.1109/ICECET63943.2025.11472202.
[12] M. H. Rahman, M. M. Anwar, and F. Hossain, “AI-driven big data and business analytics: Advancing healthcare, precision medicine, supply chain resilience, energy innovation and economic competitiveness,” J. Med. Heal. Stud., vol. 6, no. 3, pp. 205–215, 2025, doi: https://doi.org/10.32996/jmhs.2025.6.3.30.
[13] M. M. T. G. Manik, A. S. M. Saimon, M. S. Islam, M. Moniruzzaman, E. Rozario, and E. Hossin, Big Data Analytics for Credit Risk Assessment. 2025. doi: https://doi.org/10.1109/ICMLAS64557.2025.10967667.
[14] H. Imam, M. Mustafizur, T. Roy, A. Lalafaryan, A. Mkrtchyan, and V. Roy, “Intelligent and secure financial analytics platforms using advanced machine learning,” 2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp. 769–775, 2026. doi: https://doi.org/10.1109/CICN70047.2026.11594305.
[15] F. Karim et al., “Empowering women through microcredit: An analysis of the Grameen Bank model’s impact on socio-economic outcomes,” Int. J. Comput. Exp. Sci. Eng., vol. 10, no. 4, 2024, doi: https://doi.org/10.22399/ijcesen.4074.
[16] D. Alam, S. Saha, M. I. Alam, N. Sayeed, U. S. Saha, and M. Mustafizur, “Data-centric architectures for digital business and marketing technologies,” in Innovative Computing and Communications, vol. 2046, Springer, 2027, pp. 546–561. doi: https://doi.org/10.1007/978-3-032-30438-4_44.
[17] M. A. R. Chy et al., “Understanding the Relationship Between Data Governance and Business Analytics Success: A Case Study of Global Corporations,” J. Inf. Syst. Eng. Manag., vol. 9, no. 4s, 2024, doi: https://doi.org/10.52783/jisem.v9i4s.14807.
[18] M. Adnan, P. A. Momotaj, T. Roy, R. Anzum, Z. A. Tanni, and M. M. T. G. Manik, “Stream data analytics for high-velocity data processing applications,” 2026 6th International Conference on Intelligent Technologies (CONIT). IEEE, pp. 1–9, 2026. doi: https://doi.org/10.1109/CONIT69683.2026.11621735.
[19] M. M. T. G. Manik, “Scalable data mining algorithms for large-scale data repositories,” 2026 6th International Conference on Intelligent Technologies (CONIT). IEEE, pp. 1–9, 2026. doi: https://doi.org/10.1109/CONIT69683.2026.11621696.
[20] S. Bauskar, R. K. Sahoo, S. S. Boda, H. Singhai, M. M. Bakhsh, and M. Adnan, “Privacy-aware big data governance framework using blockchain,” 2025 IEEE International Conference on Emerging Trends in Computing and Communication (ETCOM). IEEE, pp. 1–9, 2025. doi: https://doi.org/10.1109/ETCOM66606.2025.11436976.
[21] P. Chakraborty et al., “Trustworthy data lakehouse design using federated learning and blockchain,” in In 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT) (pp. 1–8). IEEE, 2025, pp. 1–8. doi: https://doi.org/10.1109/ICAFT66710.2025.11453041.
[22] S. M. Orthi et al., “DataOps-oriented big data governance for automated decision pipelines,” in 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), 2025, pp. 1–8. doi: https://doi.org/10.1109/ICAFT66710.2025.11452860.
[23] R. Anzum, R. Gentyala, N. Mukhi, N. Karri, P. Krishnan, and E. Rozario, “Hierarchical learning architectures for complex data representation,” 2026 International Conference on Emerging in Computing Technologies (INCECT). IEEE, pp. 1–8, 2026. doi: https://doi.org/10.1109/INCECT72370.2026.11742577.
[24] M. J. Hossain, M. M. Bakhsh, M. A. Sami, M. I. Alam, M. M. H. Melon, and M. M. T. G. Manik, “Self-adaptive artificial intelligence systems for large-scale data-driven decision making,” 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). IEEE, pp. 1–8, 2026. doi: https://doi.org/10.1109/I3CTCON68242.2026.11507252.
[25] T. Roy, M. H. Rijvi, M. M. Bakhsh, M. Adnan, M. Nazmussakib, and E. Rozario, “Decision robustness under information noise: A machine learning perspective on managerial decision systems,” 2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp. 327–333, 2026. doi: https://doi.org/10.1109/CICN70047.2026.11594307.
[26] R. Dandigam, M. Patel, C. S. Pareek, S. M. Faysal, G. T. Alam, and M. A. Siam, “Human-in-the-loop learning for improving language model reliability,” 2026 6th International Conference on Intelligent Technologies (CONIT). IEEE, pp. 1–8, 2026. doi: https://doi.org/10.1109/CONIT69683.2026.11620666.
[27] M. E. Hossin et al., “Adaptive multi-modal AI agents: Leveraging large language models for dynamic real-world environments,” in Proceedings of International Conference on Computational Technologies for Research in Data Analytics, vol. 1968, Springer, 2026, pp. 106–119. doi: https://doi.org/10.1007/978-3-032-25694-2_10.
[28] F. B. Khair et al., “Bridging natural language and autonomous action: LLM-driven AI agents for human–machine collaboration,” in Proceedings of International Conference on Computational Technologies for Research in Data Analytics, vol. 1968, Springer, 2026, pp. 92–105. doi: https://doi.org/10.1007/978-3-032-25694-2_9.
[29] E. Rozario, M. Nazmussakib, G. T. Alam, T. Roy, M. Adnan, and M. I. Faruk, “Dynamic big data summarisation using transformer-based generative models,” 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). IEEE, pp. 1–8, 2026. doi: https://doi.org/10.1109/I3CTCON68242.2026.11507387.
[30] S. Sultana et al., “A Comparative Review of Machine Learning Algorithms in Supermarket Sales Forecasting with Big Data,” J. Ecohumanism, vol. 3, no. 8 SE-Articles, pp. 14457 – 14467, Nov. 2024, doi: https://doi.org/10.62754/joe.v3i8.6762.
[31] G. T. Alam et al., “AI-Driven Optimization of Domestic Timber Supply Chains to Enhance U.S. Economic Security,” J. Posthumanism, vol. 5, no. 1 SE-, pp. 1581–1605, Jan. 2025, doi: https://doi.org/10.63332/joph.v5i1.2083.
[32] M. Bashir, R. A. M. Rashed, M. Mustafizur, U. S. Saha, S. M. M. Uddin, and H. Imam, “Digital procurement transformation through intelligent automation,” 2026 International Conference on Computing Theory and Wireless Communications (ICCTWC). IEEE, pp. 1–7, 2026. doi: https://doi.org/10.1109/ICCTWC68241.2026.11583410.
[33] M. A. Goffer, “Leveraging Predictive Analytics In Management Information Systems To Enhance Supply Chain Resilience And Mitigate Economic Disruptions,” Educ. Adm. Theory Pract., vol. 30, no. 4, pp. 11134–11144, 2024, doi: https://doi.org/10.53555/kuey.v30i4.9641.
[34] F. Karim, M. Nazmussakib, R. M. Haque, U. S. Saha, and M. M. Bakhsh, “Data-driven operations and supply chain management using large language models,” 2026 IEEE 18th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, pp. 321–326, 2026. doi: https://doi.org/10.1109/CICN70047.2026.11594225.
[35] M. A. Miah et al., “An integrated big data and predictive analytics framework for supply chain resilience, international trade and economic growth,” J. Intell. Decis. Mak. Inf. Sci., vol. 3, no. 2, pp. 1676–1694, 2026, doi: https://doi.org/10.59543/jidmis.v3.2223.
[36] N. Sayeed, U. S. Saha, F. Karim, M. Mustafizur, S. M. M. Uddin, and M. A. R. Chy, “Artificial intelligence models for end-to-end supply chain optimization,” 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). IEEE, pp. 1–8, 2026. doi: https://doi.org/10.1109/I3CTCON68242.2026.11507407.
[37] P. Chakraborty et al., “Toward autonomous decision intelligence: Integrating explainable AI and scalable DSS architectures in modern management information systems,” J. Inf. Syst. Eng. Manag., vol. 9, no. 4s, pp. 3893–3917, 2024, doi: https://doi.org/10.52783/jisem.v9i4s.14591.
[38] N. Das et al., “Leveraging Management information Systems for Agile Project Management in Information Technology: A comparative Analysis of Organizational Success Factors,” J. Bus. Manag. Stud., vol. 5, no. 3, p. 161, 2023, doi: https://doi.org/10.32996/jbms.2023.5.3.17.
[39] M. E. Hossin et al., “Harnessing business analytics in management information systems to foster sustainable economic growth through smart manufacturing and industry 4.0,” Educ. Adm. Theory Pract., vol. 30, no. 10, pp. 730–739, 2024, doi: https://doi.org/10.53555/kuey.v30i10.9643.
[40] M. E. Hossin et al., “Digital Transformation in the USA Leveraging AI and Business Analytics for IT Project Success in the Post-Pandemic Era,” J. Posthumanism, vol. 5, no. 4 SE-, pp. 958–976, Apr. 2025, doi: https://doi.org/10.63332/joph.v5i4.1180.
[41] F. Mahmud, M. A. Goffer, H. Rahman, and G. T. Alam, “The Role of Cloud-Based Management Information Systems in Enhancing IT Project Governance and Stakeholder Collaboration,” 2025, doi: https://doi.org/10.1007/978-3-032-05548-4_1.
[42] F. Mahmud et al., “Big data and cloud computing in IT project management: A framework for enhancing performance and decision-making,” Fuel Cells Bull., vol. 2023, no. 9, pp. 1–18, 2024, doi: https://doi.org/10.52710/fcb.166.
[43] S. M. Orthi et al., “AI-augmented risk intelligence in IT project management: An empirical MIS-driven evaluation using machine learning and neural networks,” Int. J. Appl. Math., vol. 38, no. 12s, pp. 2869–2890, 2025, doi: https://doi.org/10.12732/ijam.v38i12s.1594.
[44] K. B. Siddiqa et al., “AI-Driven Project Management Systems: Enhancing IT Project Efficiency through MIS Integration,” in 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS), 2024, pp. 114–119. doi: https://doi.org/10.1109/ICPIDS65698.2024.00027.
[45] S. M. M. Uddin et al., “AI-powered business intelligence: Enhancing decision-making through predictive analytics and big data,” J. Posthumanism, vol. 6, no. 3, pp. 218–237, 2026, doi: https://doi.org/10.63332/joph.v6i3.4083.