AI-Driven Big Data Processing on Cloud Platforms for Predictive Financial Decision-Making
DOI:
https://doi.org/10.15662/IJEETR.2026.0801005Keywords:
Artificial Intelligence, Big Data, Cloud Computing, Predictive Analytics, Financial Decision-Making, Machine Learning, Data MiningAbstract
The rising complexity and magnitude of financial information in the modern digitalized world have indeed led to the application of superior technologies that enhance decision-making. This study paper discusses how Artificial Intelligence (AI) and Big Data processing can be implemented to cloud environments to improve predictive financial decision-making. The combination of AI and the cloud-based big data systems is highly promising in streamlining the financial analytics, through the provision of real-time insights, enhancing the accuracy, and lowering the operational costs. The suggested framework uses the infrastructure of cloud computing to store and to process huge amounts of financial data and uses machine learning and data mining strategies to forecast the trends, risks, and opportunities in the financial markets. The AI algorithms implemented in the structure improve the capacity to study past financial data, trace the patterns, and predict the future trends in the market. The most important to note about the frameworks are the data collection, data preprocessing, model training, predictive analytics and decision-making support, which are all supported by a scalable cloud environment. This practice will enable greater flexibility and responsiveness to the changing market circumstances, making informed financial choices. The study also shows the problems and constraints of such systems implementation, such as the data privacy issue, model interpretability, and the issue of managing cloud resources. This article provides a new paradigm of data-driven strategy in the financial industry by showing the effectiveness of AI-based big data solutions in changing the financial decision-making process through case studies and experimental findings.
References
1. Nguyen, DK, Sermpinis, G, and Stasinakis, C. Big data, artificial intelligence and machine learning: a transformative Symbiosis in favour of financial technology. Eur Financ Manag. (2023) 29:517–48. doi: 10.1111/eufm.12365
2. Wei, D. Prediction of stock price based on LSTM neural network. 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). (2019) 544–547. doi: 10.1109/AIAM48774.2019.00113
3. Rouf, N, Malik, MB, Arif, T, Sharma, S, Singh, S, Aich, S, et al. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics (Basel). (2021) 10:2717. doi: 10.3390/electronics10212717
4. Kumar, D, Sarangi, PK, and Verma, R. A systematic review of stock market prediction using machine learning and statistical techniques. Mater Today Proc. (2022) 49:3187–91. doi: 10.1016/j.matpr.2020.11.399
5. Li, G, Wang, X, Bi, D, and Hou, J. Risk measurement of the financial credit industry driven by data. J Glob Inf Manag. (2022) 30:1–20. doi: 10.4018/JGIM.308806
6. Sun, Y, and Li, J. Deep learning for intelligent assessment of financial investment risk prediction. Comput Intell Neurosci. (2022) 2022:1–11. doi: 10.1155/2022/3062566
7. Saito, T, and Gupta, S. Big data applications with theoretical models and social media in financial management. Ann Oper Res. (2022) 14:1–23. doi: 10.1007/s10479-022-05136-x
8. VenkateswaraRao, M, Vellela, S, Reddy, VB, Vullam, N, Basha Sk, K, and Roja, D. Credit investigation and comprehensive risk management system based on big data analytics in commercial banking. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). (2023) 1:2387–2391. doi: 10.1109/ICACCS57279.2023.10113084
9. Goldstein, I, Spatt, CS, and Ye, M. Big data in finance. Rev Financ Stud. (2021) 34:3213–25. doi: 10.1093/rfs/hhab038
10. Rane, NL, Choudhary, SP, and Rane, J. Artificial intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability. Stud. Econ. Bus. Relat. (2024) 5:1–22. doi: 10.48185/sebr.v5i2.1050
11. Faheem, MA, Aslam, M, and Kakolu, S. Artificial intelligence in investment portfolio optimization: a comparative study of machine learning algorithms. Int. J. Sci. Res. Arch. (2022) 6:335–42. doi: 10.30574/ijsra.2022.6.1.0131
12. Gogas, P, and Papadimitriou, T. Machine learning in economics and finance. Comput Econ. (2021) 57:1–4. doi: 10.1007/s10614-021-10094-w
13. Sezer, OB, Gudelek, MU, and Ozbayoglu, AM. Financial time series forecasting with deep learning: a systematic literature review: 2005–2019. Appl Soft Comput. (2020) 90:106181. doi: 10.1016/j.asoc.2020.106181
14. Torres, JF, Hadjout, D, Sebaa, A, Martínez-Álvarez, F, and Troncoso, A. Deep learning for time series forecasting: a survey. Big Data. (2021) 9:3–21. doi: 10.1089/big.2020.0159
15. Paricherla M et al, A. Machine learning techniques for accurate classification and detection of intrusions in computer network. Bulletin of Electrical Engineering and Informatics. 2023;12(4):2340-2347. doi:10.11591/eei.v12i4.4708
