Advanced Generative AI Frameworks for Cryptocurrency Fraud Detection and Volatility Prediction in Cloud-Based Systems
DOI:
https://doi.org/10.15662/IJEETR.2023.0506026Keywords:
Generative AI, Cryptocurrency Fraud Detection, Volatility Prediction, Blockchain Analytics, GANs, Transformers, Graph Neural Networks, Cloud Computing, Java Microservices, Deep Learning, Financial Security, Anomaly DetectionAbstract
The rapid evolution of cryptocurrency ecosystems has introduced complex challenges such as sophisticated fraud schemes and extreme price volatility. Traditional analytical models struggle to effectively detect fraudulent activities and predict volatile price movements due to the decentralized, high-frequency, and nonlinear nature of blockchain data. This study proposes an advanced generative artificial intelligence (AI) framework that integrates state-of-the-art models, including Generative Adversarial Networks (GANs), Transformer architectures, and Graph Neural Networks (GNNs), for enhanced cryptocurrency analytics.
Recent research highlights that hybrid frameworks combining graph attention mechanisms with transformer-based models significantly improve fraud detection accuracy by capturing both structural and temporal transaction patterns . Similarly, transformer-based models outperform traditional approaches such as LSTM in predicting cryptocurrency volatility, especially during high market turbulence .
The proposed system is deployed within a cloud-based architecture utilizing Java microservices, containerization, and distributed data pipelines to ensure scalability and real-time processing. By leveraging generative AI for synthetic data augmentation and anomaly detection, the framework enhances predictive performance and robustness.
The findings demonstrate that integrating generative AI with cloud-native systems significantly improves fraud detection capabilities and volatility forecasting accuracy, contributing to more secure, efficient, and reliable cryptocurrency markets.
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