Generative AI-Driven Cryptocurrency Analytics: Fraud Detection, Volatility Prediction, and Cloud-Native Java Architectures

Meenu Dave

doi:10.15662/IJRAI.2023.0605012

Authors

Meenu Dave Professor, Jagan Nath University, Jaipur, India Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0605012

Keywords:

Generative AI, Cryptocurrency Analytics, Fraud Detection, Volatility Prediction, Blockchain, GANs, Transformers, Cloud-Native Architecture, Java Microservices, Machine Learning, Financial Technology, Anomaly Detection

Abstract

The rapid expansion of cryptocurrency markets has introduced unprecedented opportunities alongside significant risks, including fraud, market manipulation, and extreme volatility. Traditional analytical methods often fail to capture the dynamic, decentralized, and high-frequency nature of blockchain ecosystems. This paper explores the integration of Generative Artificial Intelligence (AI) techniques with cryptocurrency analytics to enhance fraud detection and volatility prediction. By leveraging advanced models such as Generative Adversarial Networks (GANs), Transformers, and Variational Autoencoders (VAEs), the study demonstrates how synthetic data generation and pattern recognition can improve predictive accuracy and anomaly detection.

Furthermore, the paper proposes a cloud-native architecture implemented using Java-based microservices, enabling scalable, resilient, and real-time analytics pipelines. The architecture integrates distributed data processing, blockchain monitoring tools, and AI inference services deployed on cloud platforms. Emphasis is placed on modular design, containerization, and orchestration to ensure adaptability in rapidly evolving financial environments.

The research highlights the synergy between generative AI and cloud-native engineering in addressing key challenges in cryptocurrency ecosystems. It concludes that such integrated systems can significantly enhance security, forecasting reliability, and operational efficiency, thereby contributing to more stable and trustworthy digital financial markets.

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