Federated Deep Learning-Based Privacy-Preserving Healthcare Analytics for Distributed Medical IoT Systems

Abstract

The increasing complexity of financial markets, regulatory requirements, and risk management processes has accelerated the adoption of Artificial Intelligence (AI) and cloud-native data engineering within investment banking. Modern investment banks generate massive volumes of structured and unstructured data from market feeds, trading platforms, customer transactions, regulatory disclosures, and macroeconomic indicators. Traditional risk assessment systems often struggle to process this data efficiently, resulting in delayed insights and reduced responsiveness to market volatility. AI-powered data pipelines have emerged as a transformative solution by integrating cloud computing, automated data engineering, machine learning, and real-time analytics into a unified risk management framework. These pipelines enable continuous data ingestion, intelligent feature engineering, predictive modeling, anomaly detection, and automated decision support for credit risk, market risk, liquidity risk, and operational risk management. This study presents an experimental evaluation of AI-powered data pipelines for investment banking risk assessment. The research investigates the effectiveness of cloud-native AI architectures in improving risk prediction accuracy, processing efficiency, scalability, and decision-making performance. A comprehensive framework integrating cloud data engineering, machine learning algorithms, automated feature extraction, and real-time visualization is proposed and experimentally evaluated. Performance metrics including prediction accuracy, precision, recall, F1-score, processing latency, throughput, scalability efficiency, and risk detection rate are analyzed.