Financial fraud is a persistent problem in the finance industry that may have severe
consequences for individuals, businesses, and economies. Graph Neural Networks (GNNs) …

Tax risk behavior causes serious loss of fiscal revenue, damages the country's public
infrastructure, and disturbs the market economic order of fair competition. In recent years, tax …

Tax evasion through complex network schemes poses a significant challenge to tax
authorities, leading to substantial revenue losses. This paper aims to develop and evaluate …

In the evolving business landscape, the scope of risk factors is extremely wide, making it
impossible for all business-related risks to be captured within publicly available financial …

Tax evasion, an unlawful practice in which taxpayers deliberately conceal information to
avoid paying tax liabilities, poses significant challenges for tax authorities. Effective tax …

Federated Learning (FL) is a distributed machine learning system designed to effectively
address potential data privacy concerns, making it particularly promising for the Internet of …

This study investigates the nexus between financial development and tax evasion across
156 countries from 2000 to 2017. In contrast to previous research focusing solely on banks …

Despite the success of machine learning models, the literature lacks their applications to
identify the exploitation of non-public information. We address this gap by develo** a tool …

Extracting effective features from dynamic networks underpins the development of network-
based artificial intelligence (AI) methods and decision support systems. Despite existing …

Abstract Heterogeneous Graph Contrastive Learning (HGCL) has attracted lots of attentions
because of eliminating the requirement of node labels. The encoders used in HGCL mainly …