[HTML][HTML] A survey of tax risk detection using data mining techniques
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 …
infrastructure, and disturbs the market economic order of fair competition. In recent years, tax …
Mapping value sensitive design onto AI for social good principles
S Umbrello, I Van de Poel - AI and Ethics, 2021 - Springer
Value sensitive design (VSD) is an established method for integrating values into technical
design. It has been applied to different technologies and, more recently, to artificial …
design. It has been applied to different technologies and, more recently, to artificial …
Artificial intelligence and fraud detection
Fraud exists in all walks of life and detecting and preventing fraud represents an important
research question relevant to many stakeholders in society. With the rise in big data and …
research question relevant to many stakeholders in society. With the rise in big data and …
Value-added tax fraud detection with scalable anomaly detection techniques
The tax fraud detection domain is characterized by very few labelled data (known fraud/legal
cases) that are not representative for the population due to sample selection bias. We use …
cases) that are not representative for the population due to sample selection bias. We use …
Machine Learning Models and Applications for Early Detection
O Zapata-Cortes, MD Arango-Serna… - Sensors (Basel …, 2024 - pmc.ncbi.nlm.nih.gov
From the various perspectives of machine learning (ML) and the multiple models used in this
discipline, there is an approach aimed at training models for the early detection (ED) of …
discipline, there is an approach aimed at training models for the early detection (ED) of …
Tax evasion risk management using a Hybrid Unsupervised Outlier Detection method
Big data methods are becoming an important tool for tax fraud detection around the world.
Unsupervised learning approach is the dominant framework due to the lack of label and …
Unsupervised learning approach is the dominant framework due to the lack of label and …
Fraud detection using neural networks: A case study of income tax
Detecting tax fraud is a top objective for practically all tax agencies in order to maximize
revenues and maintain a high level of compliance. Data mining, machine learning, and …
revenues and maintain a high level of compliance. Data mining, machine learning, and …
Improving tax audit efficiency using machine learning: The role of taxpayer's network data in fraud detection
V Baghdasaryan, H Davtyan, A Sarikyan… - Applied Artificial …, 2022 - Taylor & Francis
Using the universe of Armenian business tax payers operating under a standard tax regime,
we develop a fraud prediction model based on machine learning tools, with gradient …
we develop a fraud prediction model based on machine learning tools, with gradient …
[HTML][HTML] Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms
Double tax treaties play a crucial role in shaping international economic relations, yet
predicting which country pairs are likely to sign tax treaties remains a challenge. This study …
predicting which country pairs are likely to sign tax treaties remains a challenge. This study …
An edge feature aware heterogeneous graph neural network model to support tax evasion detection
Tax evasion is an illegal activity that causes severe losses of government revenues and
disturbs the economic order. To alleviate this problem, decision support systems that enable …
disturbs the economic order. To alleviate this problem, decision support systems that enable …