An AI-Driven Cloud-Based Data Engineering Framework for Real-Time Investment Banking Analytics

Authors

  • Sathish Kaniganahali Ramareddy

Keywords:

Transformer Networks, Graph Neural Networks, Conversational AI, Semantic Intelligence, Context-Aware Reasoning, Knowledge Graphs.

Abstract

The rapid growth of financial data generated from trading systems, customer transactions, market feeds, regulatory platforms, and digital banking services has transformed the operational landscape of investment banking. Traditional data management architectures often face challenges in processing high-volume, high-velocity, and heterogeneous financial datasets in real time. Consequently, investment banks increasingly adopt cloud-native data engineering solutions integrated with artificial intelligence (AI) technologies to support scalable analytics, predictive intelligence, risk management, and data-driven decision making. Between 2020 and 2024, advancements in cloud computing, distributed data processing, machine learning, real-time streaming analytics, and financial intelligence systems significantly enhanced the capability of financial institutions to manage and analyze complex datasets. This study proposes an AI-driven cloud-based data engineering framework for real-time investment banking analytics. The framework integrates cloud infrastructure, data lakes, distributed processing engines, artificial intelligence models, machine learning algorithms, streaming data pipelines, and business intelligence platforms into a unified architecture. A systematic review of literature published between 2020 and 2024 is conducted to examine the role of AI-enabled cloud data engineering in improving investment banking operations, market intelligence, fraud detection, algorithmic trading, customer analytics, portfolio management, and regulatory compliance.

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Published

31.01.2024

How to Cite

Sathish Kaniganahali Ramareddy. (2024). An AI-Driven Cloud-Based Data Engineering Framework for Real-Time Investment Banking Analytics. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 750 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8265

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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Copyright (c) 2026 Sathish Kaniganahali Ramareddy

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