1 research outputs found
Detecting Transaction-based Tax Evasion Activities on Social Media Platforms Using Multi-modal Deep Neural Networks
Social media platforms now serve billions of users by providing convenient
means of communication, content sharing and even payment between different
users. Due to such convenient and anarchic nature, they have also been used
rampantly to promote and conduct business activities between unregistered
market participants without paying taxes. Tax authorities worldwide face
difficulties in regulating these hidden economy activities by traditional
regulatory means. This paper presents a machine learning based Regtech tool for
international tax authorities to detect transaction-based tax evasion
activities on social media platforms. To build such a tool, we collected a
dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts
with multiple properties related to transaction-based tax evasion activities.
Based on the dataset, we developed a multi-modal deep neural network to
automatically detect suspicious posts. The proposed model combines comments,
hashtags and image modalities to produce the final output. As shown by our
experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762,
outperforming any single modality models. This tool could help tax authorities
to identify audit targets in an efficient and effective manner, and combat
social e-commerce tax evasion in scale