Artificial Intelligence and Economic Resilience: A Review of Predictive Financial Modelling for Post-Pandemic Recovery in the United States SME Sector


Authors : Sakera Begum

Volume/Issue : Volume 10 - 2025, Issue 7 - July


Google Scholar : https://tinyurl.com/y2jb2yvz

Scribd : https://tinyurl.com/5dc7rx3r

DOI : https://doi.org/10.38124/ijisrt/25jul1726

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.


Abstract : Small and medium-sized enterprises (SMEs) are highly vulnerable to economic crises due to financial constraints and operational instability. The COVID-19 pandemic has exacerbated these vulnerabilities, emphasizing the need for robust financial systems. AI can help enhance resilience and financial sustainability. The purpose of this review study is to investigate how AI-driven predictive financial modelling can enable SMEs in the United States to maintain economic resilience in the aftermath of a pandemic. The findings show that AI adoption leads to considerable gains in financial decision-making, early risk detection, and resource optimization all of which are critical components of resilience. Predictive models may anticipate cash flow, evaluate credit risk, and provide SMEs with timely insights into market trends. However, challenges such as data quality and a lack of digital infrastructure may impede adoption, especially among resource-constrained or low-tech businesses. Therefore, predictive financial modelling powered by AI has transformative potential for increasing the resilience and competitiveness of United States SMEs in a dynamic and constantly developing economy.

Keywords : Algorithmic Decision-Making, AI-Driven Financial Forecasting, Data Analytics, Business Support, Economic Impact.

References :

  1. Adelakun, B. (2023). AI-driven Financial Forecasting: Innovations and Implications for Accounting Practices. ResearchGate; Fair East Publishers. https://www.researchgate.net/publication/381465681_AI-driven_financial_forecasting_innovations_and_implications_for_accounting_practices
  2. Adeyelu O. O., Ugochukwu C. E., & Shonibare M. (2024). The Impact of Artificial Intelligence on Accounting Practices: Advancements, Challenges, and Opportunities. ResearchGate; Fair East Publishers. https://www.researchgate.net/publication/379921141_the_impact_of_artificial_intelligence_on_accounting_practices_advancements_challenges_and_opportunities
  3. Adeyeri, T. B. (2024). Economic Impacts of AI-Driven Automation in Financial Services. Valley International Journal Digital Library, 12(7), 6779–6791. https://doi.org/10.18535/ijsrm/v12i07.em07
  4. Akanfe, O., Bhatt, P., & Lawong, D. A. (2025). Technology Advancements Shaping the Financial Inclusion Landscape: Present Interventions, Emergence of Artificial Intelligence and Future Directions. Information Systems Frontiers. https://doi.org/10.1007/s10796-025-10597-z
  5. Alonge, E. O., Nsisong, L., Eyo-Udo, Ubanadu B. C., & Ogunsola K. O. (2023). The Role of Predictive Analytics in Enhancing Customer Experience and Retention. The Role of Predictive Analytics in Enhancing Customer Experience and Retention. https://www.researchgate.net/publication/390111346_The_Role_of_Predictive_Analytics_in_Enhancing_Customer_Experience_and_Retention
  6. Avickson, E. K., Nyonyoh, N., & Ampaw-Asiedu, D. (2024). Financial Forecasting and Planning: Predictive Models that Support Agile Financial Planning by Adjusting Forecasts Based on Emerging Trends and Macroeconomic Factors. International Journal of Research Publication and Reviews, 5(11), 301–319. https://doi.org/10.55248/gengpi.5.1124.3115
  7. Bak, O., Shaw, S., Colicchia, C., & Kumar, V. (2020). A Systematic Literature Review of Supply Chain Resilience in Small–Medium Enterprises (SMEs): A Call for Further Research. IEEE Transactions on Engineering Management, 70(1), 1–14.
  8. Balan, G. S., Kumar, V. S., & Raj, S. A. (2025). Machine learning and artificial intelligence methods and applications for post-crisis supply chain resiliency and recovery. Supply Chain Analytics, 10, 100121. https://doi.org/10.1016/j.sca.2025.100121
  9. Belitski, M., Guenther, C., Kritikos, A. S., & Thurik, R. (2021). Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses. Small Business Economics, 58(14630), 593–609. https://doi.org/10.1007/s11187-021-00544-y
  10. Biyela N. Y., & Utete R. (2024). Agenda for future business resilience and survival avenues in crisis times: A systematic literature review of the effects of COVID-19 on SMEs’ productivity in South Africa. Social Sciences & Humanities Open, 10, 100982–100982. https://doi.org/10.1016/j.ssaho.2024.100982
  11. Boussalham, K., & Ejjami, R. (2024). Optimizing In-Store Logistics: How AI Enhances Inventory Management and Space Utilization. Journal of Next-Generation Research 5.0 (JNGR 5.0), 1(1). https://doi.org/10.70792/jngr5.0.v1i1.10
  12. Carayannis, E. G., Dumitrescu, R., Falkowski, T., Papamichail, G., & Zota, N. - R. (2025). Enhancing SME Resilience through Artificial Intelligence and Strategic Foresight: A Framework for Sustainable Competitiveness. Technology in Society, 81, 102835. https://doi.org/10.1016/j.techsoc.2025.102835
  13. Celestin, M., & Sujatha, S. (2024). Impact of Global Supply Chain Disruptions on Business Resilience: Strategies for Adapting to Pandemics and Geopolitical Conflicts. Impact of Global Supply Chain Disruptions on Business Resilience: Strategies for Adapting to Pandemics and Geopolitical Conflicts, 9(2), 44–53. https://doi.org/10.5281/zenodo.13887198
  14. Černevičienė J., & Kabašinskas A. (2024). Explainable artificial intelligence (XAI) in finance: a systematic literature review. Artificial Intelligence Review, 57(8). https://doi.org/10.1007/s10462-024-10854-8
  15. Das, D., Sarkar, A., & Debroy, A. (2022). Impact of COVID‐19 on Changing Consumer Behaviour: Lessons from an Emerging Economy. International Journal of Consumer Studies, 46(3), 692–715. wiley. https://doi.org/10.1111/ijcs.12786
  16. Drydakis, N. (2022). Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic. Information Systems Frontiers, 24. springer. https://doi.org/10.1007/s10796-022-10249-6
  17. Faisal, R., Amekudzi, C. S., Kamran, S., Fonkem, B., & Martins Awofadeju. (2023). The Impact of Digital Transformation on Small and Medium Enterprises (SMEs) in the USA: Opportunities and Challenges. IRE Journals , 7(6). https://www.researchgate.net/publication/387722419_The_Impact_of_Digital_Transformation_on_Small_and_Medium_Enterprises_SMEs_in_the_USA_Opportunities_and_Challenges
  18. Gregurec, I., Tomičić Furjan, M., & Tomičić-Pupek, K. (2021). The Impact of COVID-19 on Sustainable Business Models in SMEs. Sustainability, 13(3), 1098. https://doi.org/10.3390/su13031098
  19. Guo, Y., Liu, F., Song, J.-S., & Wang, S. (2024). Supply Chain Resilience: a Review from the Inventory Management Perspective. Fundamental Research, 5(2), 1–14. https://doi.org/10.1016/j.fmre.2024.08.002
  20. Hossain, M. R., Akhter, F., & Sultana, M. M. (2022). SMEs in Covid-19 Crisis and Combating strategies: a Systematic Literature Review (SLR) and a Case from Emerging Economy. Operations Research Perspectives, 9, 100222. ScienceDirect. https://doi.org/10.1016/j.orp.2022.100222
  21. Humphries, J. E., Neilson, C. A., & Ulyssea, G. (2020). Information frictions and access to the Paycheck Protection Program. Journal of Public Economics, 190, 104244. https://doi.org/10.1016/j.jpubeco.2020.104244
  22. Hussain, K. (2023). Harnessing AI for Predictive Accuracy in Financial Forecasting and Risk Assessment. https://doi.org/10.13140/RG.2.2.17070.73281
  23. Islam T., Islam, M., Sarkar A., Rahman, O., Paul R., & Bari, S. (2024). Artificial Intelligence in Fraud Detection and Financial Risk Mitigation: Future Directions and Business Applications. International Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.28496
  24. Joni, R., & Graepel, T. (2024). Predictive analytics and AI: Driving the next wave of risk management in financial services. ResearchGate. https://doi.org/10.13140/RG.2.2.16499.75041
  25. Modina, M., Pietrovito, F., Gallucci, C., & Formisano, V. (2023). Predicting SMEs’ default risk: Evidence from bank-firm relationship data. The Quarterly Review of Economics and Finance, 89, 254–268. https://doi.org/10.1016/j.qref.2023.04.008
  26. Nayak, S. (2022). LEVERAGING NATURAL LANGUAGE PROCESSING (NLP) AND MACHINE LEARNING FOR SENTIMENT ANALYSIS IN FINTECH: ENHANCING CUSTOMER INSIGHTS AND DECISION-MAKING. International Journal of Applied Engineering and Technology (London), 4(3), 242–260. https://www.researchgate.net/publication/387183296_LEVERAGING_NATURAL_LANGUAGE_PROCESSING_NLP_AND_MACHINE_LEARNING_FOR_SENTIMENT_ANALYSIS_IN_FINTECH_ENHANCING_CUSTOMER_INSIGHTS_AND_DECISION-MAKING
  27. Nwaimo, C. S., Adegbola, A. E., & Adegbola, M. D. (2024). Predictive analytics for financial inclusion: Using machine learning to improve credit access for under banked populations. Computer Science & IT Research Journal, 5(6), 1358–1373. https://doi.org/10.51594/csitrj.v5i6.1201
  28. Nwoke, J. (2024). Digital Transformation in Financial Services and FinTech: Trends, Innovations and Emerging Technologies. International Journal of Finance, 9(6), 1–24. https://doi.org/10.47941/ijf.2224
  29. Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024a). Artificial Intelligence in SME financial decision-making: Tools for enhancing efficiency and profitability. Open Access Research Journal of Multidisciplinary Studies, 8(1), 150–163. https://doi.org/10.53022/oarjms.2024.8.1.0056
  30. Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024b). Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management. Open Access Research Journal of Multidisciplinary Studies, 8(1), 139–149. https://doi.org/10.53022/oarjms.2024.8.1.0055
  31. Omowole, B. M., Urefe, O., Mokogwu, C., & Ewim, S. E. (2024). Integrating fintech and innovation in microfinance: Transforming credit accessibility for small businesses. International Journal of Frontline Research and Reviews, 3(1), 090-100. https://doi.org/10.56355/ijfrr.2024.3.1.0032
  32. Oni, S. B. (2025). Machine Learning Models for Predictive Financial Analysis in SMEs. Researchgate.net. https://www.researchgate.net/publication/391837095_Machine_Learning_Models_for_Predictive_Financial_Analysis_in_SMEs
  33. Pellegrino, A., & Abe, M. (2022). Digital financing for SMEs’ recovery in the post-COVID era: A bibliometric review. Frontiers in Sustainable Cities, 4. https://doi.org/10.3389/frsc.2022.978818
  34. Rakibul, M., Faraji, M. R., Rashid, M., Bhuyan, M. K., Hossain, R., & Ghose, P. (2024). Digital Transformation in SMEs Emerging Technological Tools and Technologies for Enhancing the SME’s Strategies and Outcomes. Journal of Ecohumanism, 3(4), 211–224. https://doi.org/10.62754/joe.v3i4.3594
  35. Saad, M. H., Hagelaar, G., van der Velde, G., & Omta, S. W. F. (2021). Conceptualization of SMEs’ business resilience: A systematic literature review. Cogent Business & Management, 8(1), 1938347. https://doi.org/10.1080/23311975.2021.1938347
  36. Satpathy, A. S., Sahoo, S. K., Mohanty, A., & Mohanty, P. P. (2024). Strategies for enhancements of MSME resilience and sustainability in the post-COVID-19 era. Social Sciences & Humanities Open, 11, 101223. https://doi.org/10.1016/j.ssaho.2024.101223
  37. Schönberger M. (2023). Artificial Intelligence for Small and Medium-sized Enterprises: Identifying Key Applications and Challenges. ResearchGate; RISEBA University. https://www.researchgate.net/publication/376409456_Artificial_Intelligence_for_Small_and_Medium-sized_Enterprises_Identifying_Key_Applications_and_Challenges
  38. Shafi, M., Liu, J., & Ren, W. (2020). Impact of COVID-19 Pandemic on Micro, Small, and Medium-Sized Enterprises Operating in Pakistan. Research in Globalization, 2(1), 100018. https://doi.org/10.1016/j.resglo.2020.100018
  39. Sharabati, A.-A. A., Ali, A., Allahham, M. I., Hussein, A. A., Alheet, A. F., & Mohammad, A. S. (2024). The Impact of Digital Marketing on the Performance of SMEs: An Analytical Study in Light of Modern Digital Transformations. Sustainability, 16(19), 8667–8667. MDPI. https://doi.org/10.3390/su16198667
  40. Sjödin, D., Parida, V., Palmié, M., & Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134(1), 574–587. https://doi.org/10.1016/j.jbusres.2021.05.009
  41. Smith, H. K. (2024). The Impact of COVID-19 on Supply Chain Innovation and SME Performance. https://www.researchgate.net/publication/383947651_The_Impact_of_COVID-19_on_Supply_Chain_Innovation_and_SME_Performance
  42. Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review. Cognitive Robotics, 3(1), 54–70. sciencedirect. https://doi.org/10.1016/j.cogr.2023.04.001
  43. Sophie, E. R. (2025a). Leveraging AI for cash flow management in SMEs. https://www.researchgate.net/publication/392067014_Leveraging_AI_for_cash_flow_management_in_SMEs
  44. Sophie, E. R. (2025b). “Risk Assessment and Mitigation in SME Budgeting through AI.” https://www.researchgate.net/publication/391851861_Risk_Assessment_and_Mitigation_in_SME_Budgeting_through_AI
  45. Surya, B., Menne, F., Sabhan, H., Suriani, S., Abubakar, H., & Idris, M. (2021). Economic Growth, Increasing Productivity of SMEs, and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 20. MDPI. https://doi.org/10.3390/joitmc7010020
  46. Thenmozhi V., & Krisknakumari S. (2024). Artificial Intelligence in Enhancing Operational Efficiency in Logistics and SCM. International Journal of Scientific Research in Science and Technology, 11(5), 316–323. https://doi.org/10.32628/ijsrst24115107
  47. Ugbebor, F. O., Adeteye, D. A., & Ugbebor, J. O. (2024). PREDICTIVE ANALYTICS MODELS FOR SMES TO FORECAST MARKET TRENDS, CUSTOMER BEHAVIOR, AND POTENTIAL BUSINESS RISKS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 3(3), 355–381. https://doi.org/10.60087/jklst.v3.n3.p355-381
  48. Umeaduma, C. M.-G., & Adedapo I, A. (2025). AI-powered credit scoring models: Ethical considerations, bias reduction, and financial inclusion strategies. International Journal of Research Publication and Reviews, 6(3), 6647–6661. https://doi.org/10.55248/gengpi.6.0325.12106
  49. Vijayagopal, P., Jain, B., & Viswanathan, S. A. (2024). Regulations and Fintech: A Comparative Study of the Developed and Developing Countries. Journal of Risk and Financial Management, 17(8), 324–324. https://doi.org/10.3390/jrfm17080324
  50. Wiatt, R., Marshall, M. I., Haynes, G., & Lee, Y. G. (2024). In the depths of despair: Lost income and recovery for small businesses during COVID-19. International Journal of Disaster Risk Reduction, 101, 104251. https://doi.org/10.1016/j.ijdrr.2024.104251
  51. World Bank. (2021). Small and Medium Enterprises (SMEs) Finance. World Bank; www.worldbank.org. https://www.worldbank.org/en/topic/smefinance
  52. Zhong, X., Wei, J., Li, S., & Xu, Q. (2024). Deep reinforcement learning for dynamic strategy interchange in financial markets. Applied Intelligence, 55(1). https://doi.org/10.1007/s10489-024-05965-2
  53. Zhu, H., Vigren, O., & Söderberg, I.-L. (2024). Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda. Journal of Business Research, 174. https://doi.org/10.1016/j.jbusres.2023.114494

Small and medium-sized enterprises (SMEs) are highly vulnerable to economic crises due to financial constraints and operational instability. The COVID-19 pandemic has exacerbated these vulnerabilities, emphasizing the need for robust financial systems. AI can help enhance resilience and financial sustainability. The purpose of this review study is to investigate how AI-driven predictive financial modelling can enable SMEs in the United States to maintain economic resilience in the aftermath of a pandemic. The findings show that AI adoption leads to considerable gains in financial decision-making, early risk detection, and resource optimization all of which are critical components of resilience. Predictive models may anticipate cash flow, evaluate credit risk, and provide SMEs with timely insights into market trends. However, challenges such as data quality and a lack of digital infrastructure may impede adoption, especially among resource-constrained or low-tech businesses. Therefore, predictive financial modelling powered by AI has transformative potential for increasing the resilience and competitiveness of United States SMEs in a dynamic and constantly developing economy.

Keywords : Algorithmic Decision-Making, AI-Driven Financial Forecasting, Data Analytics, Business Support, Economic Impact.

CALL FOR PAPERS


Paper Submission Last Date
31 - December - 2025

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe