Scalable Cloud-Native AI Systems for Cryptocurrency Markets: Integrating Fraud Detection and Predictive Volatility Modeling

Abstract

The rapid growth of cryptocurrency markets has introduced significant challenges, including increasing fraudulent activities and extreme price volatility. Traditional analytical systems are often unable to scale effectively or adapt to the dynamic and distributed nature of blockchain ecosystems. This research proposes a scalable cloud-native artificial intelligence framework that integrates fraud detection and predictive volatility modeling for cryptocurrency markets. The system leverages cloud computing technologies, containerization, and microservices to enable real-time data processing and model deployment. Advanced machine learning and deep learning models are employed to analyze both on-chain and off-chain data sources. Fraud detection is achieved through anomaly detection techniques that identify irregular transaction patterns and suspicious wallet behaviors. Predictive volatility modeling uses time series forecasting models, including recurrent neural networks and transformer architectures, to capture complex market dynamics. The cloud-native design ensures scalability, fault tolerance, and high availability, allowing the system to handle large volumes of streaming data. Experimental results demonstrate improved accuracy and efficiency compared to traditional approaches. The proposed framework provides valuable insights for investors, regulators, and financial institutions, enhancing risk management and decision-making in cryptocurrency markets.