Journal of Primeasia

Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
0
Citations
121.9k
Views
114
Articles
Submit
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
Journal of Primeasia

Volume 5 Number 1 2024

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 5 (1)

AI for Financial Forecasting and Strategic Decision-Making Using Predictive Analytics to Improve Budgeting, Forecasting, and Risk Management

Muslima Jahan Diba1*

+ Author Affiliations

Journal of Primeasia 5 (1) 1-8 https://doi.org/10.25163/primeasia.5110493

Submitted: 03 March 2024 Revised: 01 May 2024  Published: 10 May 2024 

Abstract

Background: Artificial Intelligence (AI) has become an essential tool for financial management because it helps improve forecasting abilities and budget creation and strategic choice evaluation. Traditional financial management systems exhibit multiple problems which result in slow reporting processes and ineffective forecasting results. AI systems allow organizations to analyze data in real-time while generating predictions through automated systems which leads to higher operational performance.

Methods: A quantitative cross-sectional survey was conducted among 350 finance professionals across banking, corporate, and public sector organizations in Bangladesh. Descriptive statistics summarized respondent demographics, while Pearson correlation (r) analyzed relationships between AI adoption and financial indicators. The study used regression analysis to find performance improvement predictors and Chi-square (χ²) tests to assess relationships between variables. Analyses were performed at p = 0.05.

Results: AI technology integration led to major improvements in financial management which resulted in a 21.2% boost in forecast accuracy and an 18.7% improvement in budget control and a 17.3% enhancement in risk mitigation. The investigation exposed AI adoption (β = 0.61, p = 0.001, χ² = 25.12) as the greatest influential forecaster shadowed by data quality (β = 0.44) and organization support (β = 0.33). The Pearson correlation analysis presented that AI adoption has strong optimistic relationships with prediction accuracy (r = 0.82, p = 0.01) and budget control (r = 0.77, p = 0.01).

Conclusion: The research displays AI expertise delivers substantial developments to financial prediction and working efficiency and planned decision-making procedures.

Keywords: Financial Forecasting, Artificial Intelligence, Predictive Analytics, Risk Management, Strategic Decision-Making

References

Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: the application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 308(1–2), 7–39. https://doi.org/10.1007/s10479-020-03620-w

Alsaifi, K., Elnahass, M., & Salama, A. (2019). Carbon disclosure and financial performance: UK environmental policy. Business Strategy and the Environment, 29(2), 711–726. https://doi.org/10.1002/bse.2426

Al-Shari, H. A., & Lokhande, M. A. (2023). The relationship between the risks of adopting FinTech in banks and their impact on the performance. Cogent Business & Management, 10(1). https://doi.org/10.1080/23311975.2023.2174242

Al-Surmi, A., Bashiri, M., & Koliousis, I. (2021). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464–4486. https://doi.org/10.1080/00207543.2021.1966540

Aseeri, M., & Kang, K. (2023). Organisational culture and big data socio-technical systems on strategic decision making: Case of Saudi Arabian higher education. Education and Information Technologies, 28(7), 8999–9024. https://doi.org/10.1007/s10639-022-11500-y

Bhargava, A., Bester, M., & Bolton, L. (2020). Employees’ perceptions of the implementation of robotics, Artificial intelligence, and Automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106–113. https://doi.org/10.1007/s41347-020-00153-8

Boute, R. N., Gijsbrechts, J., & Van Mieghem, J. A. (2021). Digital Lean Operations: smart automation and artificial intelligence in financial services. In Springer series in supply chain management (pp. 175–188). https://doi.org/10.1007/978-3-030-75729-8_6

Chowdhury, S., Rodriguez-Espindola, O., Dey, P., & Budhwar, P. (2022). Blockchain technology adoption for managing risks in operations and supply chain management: evidence from the UK. Annals of Operations Research, 327(1), 539–574. https://doi.org/10.1007/s10479-021-04487-1

Danese, P., & Kalchschmidt, M. (2010). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204–214. https://doi.org/10.1016/j.ijpe.2010.09.006

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Grosu, V., Cosmulese, C. G., Socoliuc, M., Ciubotariu, M., & Mihaila, S. (2023). Testing accountants’ perceptions of the digitization of the profession and profiling the future professional. Technological Forecasting and Social Change, 193, 122630. https://doi.org/10.1016/j.techfore.2023.122630

Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573–1590. https://doi.org/10.1080/09537287.2021.1882690

Hsu, M., Chang, C., & Zeng, J. (2022). Automated text mining process for corporate risk analysis and management. Risk Management, 24(4), 386–419. https://doi.org/10.1057/s41283-022-00099-6

Hsu, M., Hsin, Y., & Shiue, F. (2021). Business analytics for corporate risk management and performance improvement. Annals of Operations Research, 315(2), 629–669. https://doi.org/10.1007/s10479-021-04259-x

Ibrahim, S., & Lloyd, C. (2010). The association between non-financial performance measures in executive compensation contracts and earnings management. Journal of Accounting and Public Policy, 30(3), 256–274. https://doi.org/10.1016/j.jaccpubpol.2010.10.003

Ittner, C. D., & Michels, J. (2017). Risk-based forecasting and planning and management earnings forecasts. Review of Accounting Studies, 22(3), 1005–1047. https://doi.org/10.1007/s11142-017-9396-0

Johnson, M., Jain, R., Brennan-Tonetta, P., Swartz, E., Silver, D., Paolini, J., Mamonov, S., & Hill, C. (2021). Impact of big data and artificial intelligence on industry: Developing a Workforce Roadmap for a data Driven economy. Global Journal of Flexible Systems Management, 22(3), 197–217. https://doi.org/10.1007/s40171-021-00272-y

Khalifa, K., Al-Hashimi, M., & Hamdan, A. (2023). The factors affecting the adoption of artificial intelligence technologies in organizations. In Internet of things (pp. 237–265). https://doi.org/10.1007/978-3-031-35525-7_15

Li, X., Sigov, A., Ratkin, L., Ivanov, L. A., & Li, L. (2023). Artificial intelligence applications in finance: a survey. Journal of Management Analytics, 10(4), 676–692. https://doi.org/10.1080/23270012.2023.2244503

Mahalakshmi, V., Kulkarni, N., Kumar, K. P., Kumar, K. S., Sree, D. N., & Durga, S. (2021). The Role of implementing Artificial Intelligence and Machine Learning Technologies in the financial services Industry for creating Competitive Intelligence. Materials Today Proceedings, 56, 2252–2255. https://doi.org/10.1016/j.matpr.2021.11.577

Makhija, P., & Chacko, E. (2021). Efficiency and Advancement of Artificial Intelligence in Service Sector with Special Reference to Banking Industry. In Springer eBooks (pp. 21–35). https://doi.org/10.1007/978-981-16-3250-1_2

Met, I., Kabukçu, D., Uzunogullari, G., Soyalp, Ü., & Dakdevir, T. (2019). Transformation of Business Model in Finance Sector with Artificial Intelligence and Robotic Process Automation. In Contributions to management science (pp. 3–29). https://doi.org/10.1007/978-3-030-29739-8_1

Michaels, A., & Grüning, M. (2017). Relationship of corporate social responsibility disclosure on information asymmetry and the cost of capital. Journal of Management Control, 28(3), 251–274. https://doi.org/10.1007/s00187-017-0251-z

Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403

Nguyen, D. K., Sermpinis, G., & Stasinakis, C. (2022). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2), 517–548. https://doi.org/10.1111/eufm.12365

Petrozziello, A., Troiano, L., Serra, A., Jordanov, I., Storti, G., Tagliaferri, R., & La Rocca, M. (2022). Deep learning for volatility forecasting in asset management. Soft Computing, 26(17), 8553–8574. https://doi.org/10.1007/s00500-022-07161-1

Rana, N. P., Chatterjee, S., Dwivedi, Y. K., & Akter, S. (2021). Understanding dark side of artificial intelligence (AI) integrated business analytics: assessing firm’s operational inefficiency and competitiveness. European Journal of Information Systems, 31(3), 364–387. https://doi.org/10.1080/0960085x.2021.1955628

Suto, M., & Takehara, H. (2020). Corporate social responsibility intensity, management earnings forecast accuracy, and investor trust: Evidence from Japan. Corporate Social Responsibility and Environmental Management, 27(6), 3047–3059. https://doi.org/10.1002/csr.2022

Vibhakar, N. N., Tripathi, K. K., Johari, S., & Jha, K. N. (2020). Identification of significant financial performance indicators for the Indian construction companies. International Journal of Construction Management, 23(1), 13–23. https://doi.org/10.1080/15623599.2020.1844856

Walter, S. (2023). AI impacts on supply chain performance: a manufacturing use case study. Discover Artificial Intelligence, 3(1). https://doi.org/10.1007/s44163-023-00061-9


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
42
View
0
Share

Stay connected