Abstract
This paper explored the paradigm shift of adopting Artificial Intelligence (AI), high-speed cloud systems, and Big Data analytics in financial decision support systems (DSS). The conventional statistical models were no longer effective in real-time prediction as the financial sector experienced growing data complexity and volatility in the markets. The study examined how dynamic, predictive, and prescriptive analytics replaced the traditional, historical data processing. In particular, the paper has looked at the effectiveness of Deep Learning architectures, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, in processing time-series financial data. Moreover, the study also measured the importance of data warehousing and cloud migration strategies in facilitating real-time scalable stream processing. Findings revealed that although AI-based models were very effective in improving the accuracy of the forecast, especially in the fields of credit risk analysis and algorithmic trading, they also brought considerable difficulties in terms of algorithmic bias, model interpretability (Black Box problems), and data privacy. Hence, this paper proposed a detailed model of aligning AI functions with effective data management to maximize strategic decision-making within the current financial organizations.
Keywords
Artificial Intelligence; Financial Forecasting; Machine Learning; Decision Support Systems; Deep Learning; Predictive Analytics; Financial Technology.
Citations
IRE Journals:
Chidere Amaka Maureen "AI-Driven Financial Forecasting and Decision Support Systems" Iconic Research And Engineering Journals Volume 9 Issue 7 2026 Page 1642-1648 https://doi.org/10.64388/IREV9I7-1713728
IEEE:
Chidere Amaka Maureen
"AI-Driven Financial Forecasting and Decision Support Systems" Iconic Research And Engineering Journals, 9(7) https://doi.org/10.64388/IREV9I7-1713728
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12