CBIFM-Cloud-Based Intelligent Financial Management System for Small and Medium Enterprises

doi:10.23940/ijpe.26.10.p3.580588

Abstract

Abstract: The major problems that SMEs experience in carrying out their financial management tasks include fragmentation of accounting system, lack of technological capacity and lack of intelligent financial decision making tools. Even though cloud computing and artificial intelligence technologies have individually contributed significantly towards the development of effective financial management strategies, there still lacks a comprehensive solution that brings together automated accounting, financial analysis and intelligent decision making. In this research paper, the proposal of a Cloud Based Intelligent Financial Management System (CBIFM) is discussed where cloud computing, cloud accounting, AI and financial analytics have been combined within a single platform to help SMEs in their financial management. Some of the functionalities that can be achieved using CBIFM include automated transactions processing, budget management, expense tracking, cash flow predictions and dashboard visualization among others. The proposed framework incorporates cloud based financial management modules, artificial intelligence engines and databases. Performance of the proposed framework was evaluated using the Accounting Data for Financial Management dataset while comparing its performance with a conventional cloud accounting system using metrics including financial reporting accuracy, accuracy in predicting cash flows, effectiveness of budget usage, processing time of transactions, and response time of the system. The experimental results show that the suggested CBIFM framework was successful in reaching an accuracy of 97.6% in financial reporting, 95.8% in cash flow prediction, and 96.3% in budget utilization, while decreasing the transaction processing time from 245 ms to 168 ms. It can be said that the integration of cloud computing along with AI-based financial analysis leads to a more effective financial management and decision-making process, which will help ensure sustainability of SMEs.

Key words: cloud-based financial management system (CBIFM), small and medium enterprises (SMEs), cloud computing, artificial intelligence, cloud accounting, financial analytics, cash flow prediction, intelligent decision support

Reema Sharma, Pragati Bhati, Deepak Bansal, and Thangjam Ravichandra. CBIFM-Cloud-Based Intelligent Financial Management System for Small and Medium Enterprises [J]. Int J Performability Eng, 2026, 22(10): 580-588.

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References

[1] Ogedengbe A.O., Jejeniwa T.O., Friday S.C., and Olatunji H., 2024. Framework for digitally transforming financial management systems in SME and public sector organizations.
[2] Jiang J.,2024. A study on the digital transformation trends in financial management for small and micro enterprises. International Journal of Global Economics and Management,3(1), pp. 355-363.
[3] Deng J.,2022. The informatization of small and medium‐sized enterprises accounting system based on sensor monitoring and cloud computing.Mobile Information Systems, 2022(1), 5007837.
[4] OSAH A.A.,2025. Impact of cloud-based accounting software on financial management efficiency in small firm businesses in port harcourt. International Journal of Economics, Finance, Accounting and Management,1(2), pp. 67-77.
[5] Marston S., Li Z., Bandyopadhyay S., Zhang J., and Ghalsasi A., 2011. Cloud computing—the business perspective. Decision Support Systems,51(1), pp. 176-189.
[6] Mihai M.S., and Duţescu A., 2022. How cloud accounting and integrated services based on AI can impact accounting companies?. In Proceedings of the International Conference on Business Excellence,16(1), pp. 849-858.
[7] Chitra M., Surianarayanan R., Mahamuni V.S., Mohammed S., Keno M.T., and Boopathi S., 2025. Study on cloud computing-empowered small and medium enterprises. InEssential Information Systems Service Management, pp. 189-220.
[8] Lee I., and Shin Y.J., 2018. Fintech: ecosystem, business models, investment decisions, and challenges. Business Horizons,61(1), pp. 35-46.
[9] Al-Mutawa B., and Saeed Al Mubarak M.M., 2024. Impact of cloud computing as a digital technology on SMEs sustainability. Competitiveness Review: an International Business Journal,34(1), pp. 72-91.
[10] Ayankoya M.B., Omotoso S.S., and Ogunlana A.A., 2025. Data driven financial optimization for small and medium enterprises (SMEs): A framework to improve efficiency and resilience in US local economies. International Journal of Management and Organizational Research,4(4), pp. 90-97.
[11] Putri E., Bandi B., Widarjo W., and Arifin T., 2025. The value of cloud accounting for MSMEs: a technology-organization-environment (TOE) framework perspective.Cogent Business & Management, 12(1), 2494712.
[12] Gurbuz A.V.,2025. The Role of Cloud-Based CRM Systems in Enhancing SMEs' Financial Management Capacities by Efficiently Collecting, Analyzing, and Utilizing Customer Data (Doctoral dissertation, Dublin, National College of Ireland).
[13] Ghasemi R., Mohaghar A., and Dehghanian M.M., 2025. Advancing intelligent supply chain management in the industry 4.0 era: A meta-synthesis analysis. Industrial Management Journal,17(2), pp. 117-148.
[14] Nguyen Phu G., Hoang Thi T., and Tran Nguyen Bich H., 2025. The impact of cloud computing technology on cloud accounting adoption and financial management of businesses.Humanities and Social Sciences Communications, 12(1), 851.

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