A Unified AI Approach for Crypto Markets: Fraud Detection, Transaction Prediction, and Volatility Modeling Using Java Microservices

Giuseppe Attardi; Giuseppe Attardi

doi:10.15662/IJAESIT.2023.0605002

Articles

A Unified AI Approach for Crypto Markets: Fraud Detection, Transaction Prediction, and Volatility Modeling Using Java Microservices

Giuseppe Attardi

Senior Systems Engineer, Italy

Abstract

The rapid expansion of cryptocurrency markets has introduced complex challenges, including fraud detection, transaction prediction, and volatility modeling. Traditional monolithic systems and statistical approaches are insufficient to handle the scale, speed, and dynamic nature of blockchain ecosystems. This research proposes a unified artificial intelligence framework leveraging Java-based microservices architecture to address these challenges effectively. The system integrates advanced machine learning and deep learning techniques to analyze both on-chain and off-chain data streams in real time. Fraud detection is achieved using anomaly detection models that identify suspicious transaction patterns and wallet behaviors. Transaction prediction employs sequence learning models to forecast transaction flows and user activities, while volatility modeling uses time series forecasting techniques to predict market fluctuations. The microservices architecture enables modularity, scalability, and independent deployment of each analytical component, ensuring flexibility and resilience. Technologies such as Spring Boot, Apache Kafka, and RESTful APIs facilitate seamless communication between services. Experimental evaluation demonstrates improved accuracy, scalability, and fault tolerance compared to traditional architectures. The proposed approach offers a robust solution for investors, regulators, and financial institutions, enhancing decision-making and risk management in cryptocurrency markets.

Article Information

Journal	International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
Volume (Issue)	Vol. 6 No. 5 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
DOI	https://doi.org/10.15662/IJAESIT.2023.0605002
Pages	12430-12439
Published	September 16, 2023
Copyright	Creative Commons Attribution 4.0 International License
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Giuseppe Attardi (2023). A Unified AI Approach for Crypto Markets: Fraud Detection, Transaction Prediction, and Volatility Modeling Using Java Microservices. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 5 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 12430-12439. https://doi.org/10.15662/IJAESIT.2023.0605002

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