AI-Driven Decision Support Systems for Optimizing Working‎ Capital and Customer Experience in The U.S.: A Transaction‎ Based Simulation Framework for SMEs

  • Authors

    • Md Rasibul Islam Department of Public Administration, Gannon University, Erie, PA, USA
    • Dil Tabassum Subha Master of Science in Business Analytics, Grand Canyon University, USA
    • Md Toushif Pramanik Master of Embedded Software Engineering, Gannon University, Erie, PA, USA
    • Mahuma Akter Master of Science in Cybersecurity (MSCS), Washington University of Science and Technology (WUST)
    • Md Murshid Reja Sweet Department of Management Science and Quantitative Methods, Gannon University, USA
    • Md Sharif Robbani School of Engineering & Technology at Western Illinois University, Macomb, IIllinois, USA
    • Tapan ‎Chandra Ghos School of Engineering & Technology at Western Illinois University, Macomb, IIllinois, USA
    • Md Abdul Fahim Zeeshan Strategic communication, Gannon University, Erie, PA, USA
    https://doi.org/10.14419/1rk85s11

    Received date: February 1, 2026

    Accepted date: February 24, 2026

    Published date: March 8, 2026

  • Cash Flow Stress; Customer Behavior; Decision Support System; Retail Analytics; SMEs‎.

  • Abstract

    Running an SME often feels like walking a tightrope. You need enough cash to cover day-to-day expenses, but you also want to keep customers happy, and that can be tricky when demand jumps around unexpectedly. Most of the tools out there don’t make this easier. They ‎stick to fixed rules, ignore what your customers are actually doing, and rarely adjust when things change. That means decisions have to be ‎made in real time with little guidance, which can be stressful for managers trying to keep everything balanced. In this study, we explore an ‎AI-based system designed to predict short-term cash-flow stress and guide operational decisions that account for customers, using transaction-level retail data. Weekly financial indicators for each SME are combined with customer behavior signals drawn from purchase patterns, ‎frequency, and inferred payment risk. We test several machine learning models using validation that respects the time order of the data and ‎feed their predictions into a simulation framework that compares simple, risk-aware, and mixed decision strategies. The results show that a ‎straightforward, interpretable classification model can detect cash flow stress almost perfectly, outperforming more complex approaches. ‎Interestingly, while customer behavior features do not make the predictions more accurate, they are crucial when making actual decisions ‎based on those predictions. Simulations of operational policies indicate that hybrid, stress-aware rules outperform naive approaches, both in ‎maintaining revenue and in making balanced approval decisions during stressful periods. In the end, the main contribution of AI here is less ‎about raw predictive power and more about providing structured guidance that incorporates customer behavior to help SMEs manage working capital in uncertain conditions‎.

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  • How to Cite

    Islam, M. R., Subha, D. T., Pramanik , M. T., Akter, M., Sweet, M. M. R., Robbani , M. S. ., Ghos , T. ‎Chandra ., & Zeeshan , M. A. F. . (2026). AI-Driven Decision Support Systems for Optimizing Working‎ Capital and Customer Experience in The U.S.: A Transaction‎ Based Simulation Framework for SMEs. International Journal of Accounting and Economics Studies, 13(2), 441-454. https://doi.org/10.14419/1rk85s11