Intelligent Crypto Market Analysis Using Generative Models: Integrating Fraud Detection, Volatility Forecasting, and Blockchain Analytics

Abstract

The rapid evolution of cryptocurrency markets has introduced unprecedented opportunities alongside significant risks, including fraud, extreme volatility, and lack of regulatory oversight. Traditional analytical techniques often fail to capture the dynamic and complex nature of blockchain-based financial ecosystems. This study proposes an intelligent crypto market analysis framework leveraging generative models to enhance fraud detection, volatility forecasting, and blockchain analytics. Generative models, such as Generative Adversarial Networks (GANs) and transformer-based architectures, enable the synthesis of realistic market patterns and anomaly detection through unsupervised learning. The framework integrates on-chain transaction data, off-chain market signals, and sentiment analysis to provide a holistic understanding of crypto market behavior. Fraud detection is enhanced through anomaly detection in transaction graphs, while volatility forecasting is improved using sequence modeling of price trends and macroeconomic indicators. Additionally, blockchain analytics enables the identification of suspicious wallet clusters and transaction flows. Experimental results demonstrate that the proposed approach outperforms traditional statistical and machine learning models in accuracy, adaptability, and scalability. This research contributes to the development of more secure and predictive crypto ecosystems, aiding investors, regulators, and financial institutions in decision-making processes.