Machine Learning-Driven Methods for High-Frequency Analytics in Financial Technology
Keywords:
High-frequency analytics, machine learning, financial technology, predictive modeling, anomaly detection, trade optimization, deep learning, ensemble methodsAbstract
The emergence of high-frequency analytics (HFA) in financial technology (FinTech) has revolutionized decision-making by leveraging machine learning (ML) algorithms. This paper explores the integration of ML-driven methods in HFA, discussing their effectiveness in predictive modeling, anomaly detection, and trade optimization. The results reveal significant advancements in applying ML models, particularly deep learning and ensemble techniques, which enhance the speed and precision of high-frequency financial applications. Tables and figures illustrate comparative results and highlight the impact of these methods on real-world FinTech ecosystems.
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Copyright (c) 2025 Dr. V. ANTONY JOE RAJA (Author)
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