Open Access
A Hybrid Computational Model for Efficient Large-Scale Financial Data Analysis within Big Data Ecosystems
4
Department of Computer Science Kyoto International Junior University
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Faculty of Information Engineering Osaka Regional Technical University
Abstract
The rapid expansion of digital financial transactions, algorithmic trading platforms, mobile banking systems, and cloud-enabled financial infrastructures has intensified the need for scalable and intelligent analytical models capable of processing large-scale financial datasets in real time. Traditional financial analytical systems are increasingly inadequate for handling the velocity, variety, and volume of modern financial information generated across heterogeneous digital ecosystems. This research proposes a hybrid computational model for efficient large-scale financial data analysis within big data ecosystems by integrating distributed processing architectures, predictive analytics, artificial intelligence mechanisms, and data-driven decision frameworks. The study investigates the theoretical and operational foundations of big data analytics in financial environments while examining computational efficiency, predictive capability, fraud detection performance, and risk management optimization. A comprehensive literature synthesis based exclusively on prior scholarly works demonstrates the evolution of big data technologies in financial systems and highlights persistent gaps related to scalability, interoperability, governance complexity, and predictive consistency. The proposed hybrid model combines distributed storage mechanisms, machine learning-assisted analytical layers, stream-processing capabilities, and financial intelligence modules to improve analytical responsiveness and operational adaptability. The study further evaluates the implications of the model across risk prediction, market forecasting, fraud identification, customer intelligence, and institutional decision-making. Findings indicate that hybrid computational infrastructures significantly improve analytical efficiency, predictive accuracy, and decision responsiveness compared to conventional financial data processing frameworks. The paper contributes to financial analytics research by presenting an integrated architecture that aligns computational scalability with strategic financial intelligence requirements within contemporary big data ecosystems.
How to Cite
Dr. Haruto Nishimura, & Dr. Aiko Fujimoto. (2026). A Hybrid Computational Model for Efficient Large-Scale Financial Data Analysis within Big Data Ecosystems. Frontiers in Emerging Artificial Intelligence and Machine Learning, 3(05), 09–21. Retrieved from https://irjernet.com/index.php/feaiml/article/view/402
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