An Intelligent Financial Fraud Detection Model Based on Dilated Convolution and Generative Adversarial Network

With the widespread popularization of financial services, such as electronic payment, financial fraud has emerged as a pressing societal issue. Conventional methods of detecting financial fraud are often constrained by limitations in feature extraction and model generalization capabilities, hindering their ability to effectively respond to complex financial activity scenarios. To address this challenge, this work introduces an intelligent financial fraud detection model that integrates dilated convolution with a generative adversarial network. First, a dilated convolutional neural network is employed to extract features from transaction data. This process converts the transaction data into high-dimensional feature representations, enabling the capture of crucial information within the transactions. Subsequently, a generative adversarial network is leveraged to detect anomalies in the transaction data. Here, the generator is tasked with creating disguised representations of legitimate transaction data, while the discriminator is responsible for distinguishing between authentic and forged transaction data. Through iterative training of this model, the distribution characteristics of real transaction data are learned, enabling the detection of abnormal transaction data that does not conform to these patterns. Consequently, effective detection of financial fraud is achieved. The research results demonstrate that the proposed model not only achieves significant performance improvements on the experimental dataset but also exhibits robust generalization capabilities in real-world financial transactions.