Telecom Fraud Detection Based on Deep Learning

Telecom fraud has increasingly exhibited trends of complexity, concealment, and intelligence in recent years, posing serious threats to users' assets and social order. Traditional antifraud techniques are no longer sufficient to counter the current sophisticated fraud tactics. This paper proposes a deep learningbased telecom fraud detection method that integrates time series analysis and graph neural networks to construct a multidimensional data feature model. Experimental results demonstrate that deep learning models exhibit significant advantages in fraud detection, pattern discovery, and real-time warning, highlighting their potential for practical application in real-world scenarios.