Open Access
Navigating the Incremental Frontier: A Comprehensive Framework for Uplift Modeling, Business Intelligence Integration, And Causal Inference in Financial Decision Systems
4
Department of Data Science and Artificial Intelligence, Kyoto University, Kyoto, Japan
Abstract
In the contemporary landscape of financial management and corporate strategy, the transition from traditional descriptive analytics to advanced prescriptive modeling represents a significant paradigm shift. This research article explores the integration of uplift modeling-alternatively known as incremental value modeling-within the broader framework of Business Intelligence (BI) and data mining. While traditional propensity models focus on predicting the absolute probability of a customer action, uplift modeling seeks to isolate the causal effect of a specific intervention by identifying truly responsive individuals. This study synthesizes diverse methodologies, including meta-learners for heterogeneous treatment effects, Bayesian nonparametric modeling, and fuzzy clustering-based financial data mining. By examining the strategic impact of BI on organizational learning and financial performance, particularly in capital-constrained environments, the paper establishes a robust theoretical and practical foundation for the next generation of "decision engines." The analysis extends to the robustness of supply chains under disruption and the role of IoT-driven data visualization in corporate finance. The findings suggest that by shifting the analytical focus from "who will buy" to "who will buy because of the treatment," organizations can drastically improve resource allocation and financial report quality. The research concludes with a comprehensive design for an enterprise financial decision support system that leverages big data management and artificial intelligence to mitigate the risks associated with voluntary buyers and non-responsive prospects.
Keywords
Uplift Modeling,
Business Intelligence,
Causal Inference,
Financial Data Mining
References
Hansotia, B., & Rukstales, B. (2002). Incremental value modeling. Journal of Interactive Marketing, 16(3), 35–46. https://doi.org/10.1002/dir.10035
Haupt, J., Jacob, D., Gubela, R., & Lessmann, S. (2019). Affordable uplift: Supervised randomization in controlled experiments. In ICIS 2019 international conference on information system (ICIS). AIS. https://aisel.aisnet.org/icis2019/data_science/data_s cience/24
Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational & Graphical Statistics, 20(1), 217–240. https://doi.org/10.1198/jcgs.2010.08162
Sharma, R., & Patel, V. (2025). Artificial Intelligence and Business Intelligence Integration for Enhanced Financial Decision-Making
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011).MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8).https://doi.org/10.18637/jss.v042.i08
Huang, J. (2021). The characteristics of urban soil deposition and financial management of state-owned assets based on big data system. Arabian Journal of Geosciences, 14(14), 1–16.
Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics, 86(1), 4–29.https://doi.org/10.1162/003465304323023651
Jaskowski, M., & Jaroszewicz, S. (2012). Uplift modeling for clinical trial data. In ICML workshop on clinical data analysis: Edinburgh.
Kane, K., Lo, V. S., & Zheng, J. (2014). Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods. Journal of Marketing Analytics, 2(4), 218–238. https://doi.org/10.1057/jma.2014.18
Krishnan, G., Bhat, A. K., & Shah, J. (2025). Decision engine: Propensity prediction in the financial industry based on customer data features. In Artificial Intelligence and Sustainable Innovation (pp. 107-112). CRC Press.
Kumar, P. (2020). BI with and without data mining-a comparative study. Lal Bahadur Shastri Institute of Management.
Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Meta-learners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165. https://doi.org/10.1073/pnas.1804597116
Lai, Y., Wang, K., Ling, D., Shi, H., & Zhang, J. (2006). Direct marketing when there are voluntary buyers. In Sixth international conference on data mining (ICDM'06) (pp. 922–927). IEEE. https://doi.org/10.1109/ICDM.2006.54
Lapura, E. V. F., Fernandez, J. K. J., Pagatpat, M. J. K., & Dinawanao, D. D. (2018). Development of a university financial data warehouse and its visualization tool. Procedia Computer Science, 135, 587-595. https://doi.org/10.1016/j.procs.2018.08.229
Li, K., & Chen, Y. (2022). Fuzzy clustering-based financial data mining system analysis and design.International Journal of Foundations of Computer Science, 33(06n07), 603–624. https://doi.org/10.1142/S0129054122420060
Schuetz, C. G., & Schrefl, M. (2023). Conceptualizing analytics: An overview of BI and analytics from a conceptual-modeling perspective. Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective, 311–336. https://doi.org/10.1007/978-3-662-65004-2_13
Shahid, M. B., Sheikh, U., Raza, B., & Javaid, Q. (2016). Application of data warehouse in real life: State-of-the-art survey from user preferences’ perspective. International Journal of Advanced Computer Science and Applications, 7(4), 415-426. https://doi.org/10.14569/IJACSA.2016.070455
Shao, C., Yang, Y., & Juneja, S. (2022). IoT data visualization for BI in corporate finance. Information Processing & Management, 59(1), 102736. https://doi.org/10.1016/j.ipm.2021.102736
Sjarif, N. N. A., Azmi, N. F. M., Yuhaniz, S. S., & Wong, D. H. T. (2021). A review of market basket analysis on BI and data mining. International Journal of BI and Data Mining, 18(3), 383-394. https://doi.org/10.1504/IJBIDM.2021.114475
Tavera Romero, C. A., Ortiz, J. H., Khalaf, O. I., & Prado, A. R. (2021). Web application commercial design for financial entities based on BI. Computers, Materials & Continua, 67(3), 3177-3188. https://doi.org/10.32604/cmc.2021.014738
Tripathi, A., Bagga, T., & Aggarwal, R. K. (2020). Strategic impact of BI: A review of literature. Prabandhan: Indian Journal of Management, 13(3), 35–48.
Yang, M., Sulaiman, R., Yin, Y., Mallamaci, V., & Alrabaiah, H. (2022). The effect of business intelligence, organizational learning and innovation on the financial performance of innovative companies located in Science Park. Information Processing & Management, 59(2), 102852. https://doi.org/10.1016/j.ipm.2021.102852
Yang, N. (2022). Financial big data management and control and artificial intelligence analysis method based on data mining technology. Wireless Communications and Mobile Computing, 7596094. https://doi.org/10.1155/2022/7596094
Zhang, X., Shi, Y., Zhang, P., Xu, F., & Jiang, C. (2023). System dynamics modeling and robustness analysis for capital-constrained supply chain under disruption. Industrial Management & Data Systems, 123(2), 492–514.
Zhang, Y. G., Dang, M. Y., & Albritton, M. D. (2024). Delivering a business analytics course focused on data mining for both technical and non-technical students. Journal of Information Systems Education, 35(1), 86-98. https://doi.org/10.62273/MWCG1518
Upadhyay, H. (2025). Consumer Experience Trends Based on AI Features: A Comprehensive Analysis of Conversational AI, Personalization Engines, and Voice AI. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(11), 6–15. https://doi.org/10.64917/feaiml/Volume02Issue11-02
Zhou, J., San, O. T., & Liu, Y. (2023). Design and implementation of enterprise financial decision support system based on business intelligence. International Journal of Professional Business Review, 8(4), e0873. https://doi.org/10.26668/businessreview/2023.v8i4. 873
Zraqat, O. M. (2020). The moderating role of BI in the impact of big data on financial reports quality in Jordanian telecom companies. Modern Applied Science, 14(2), 71-85.https://doi.org/10.5539/mas.v14n2p71
Shounik, S. (2025). The Great DTC Reset as Stress Management: Evidence that Wholesale Re-Expansion Reduces "Operating Tail Risk" in Consumer Brands.. Advances in Consumer Research, 2(6), 1221-1231.
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