Melola Discovery

← Melola Discovery

Akses terbuka Jurnal

Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes

Aptisi Transactions on Technopreneurship (ATT) · Vol. 6 No. 3 (2024)

DOI
10.34306/att.v6i3.436
ISSN
2656-8888, 2655-8807
Terbit
2024-09-09
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

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 an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.

Kata kunci

Jaringan kolaborasi para penulis

31 penulis · 75 tautan kepenulisan bersama · 20 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Gunadarma University
  • Universitas Raharja
  • Saarland University
  • Universitas PGRI Palembang
  • Universitas Simalungun
  • Universitas Esa Unggul
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Rina RefiantiGunadarma University2
Asep JuarnaGunadarma University1
Adang SuhendraGunadarma University1
Tubagus Maulana KusumaGunadarma University1
Rifiana AriefGunadarma University1
Hustinawaty HustinawatyGunadarma University1
Marviola Grace HardiniUniversitas Raharja1
Ihsan Nuril HikamUniversitas Raharja1
Hetilaniar HetilaniarUniversitas PGRI Palembang1
Semaria Eva Elita GirsangUniversitas Simalungun1
Untung RahardjaUniversitas Raharja1
Prihandoko PrihandokoGunadarma University1
Nur AzizahUniversitas Raharja1
Po Abas SunaryaUniversitas Raharja1
Charlotte PashaAfiliasi tidak diketahui1
Riya WidayantiUniversitas Esa Unggul1
Avinanta TariganGunadarma University1
Diana IkasariGunadarma University1
Witta Listiya NingrumGunadarma University1
Koko BachrudinGunadarma University1
Dede Herman SuryanaGunadarma University1
Robert RobertGunadarma University1
Suryadi HarmantoGunadarma University1
Alfharizky FauziGunadarma University1
Imam Bil QisthiGunadarma University1
Zalita Nadya UtamiGunadarma University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes</dc:title>
	<dc:creator xml:lang="en">Nuryani, Nuryani</dc:creator>
	<dc:creator xml:lang="en">Mutiara, Achmad Benny</dc:creator>
	<dc:creator xml:lang="en">Wiryana, I Made</dc:creator>
	<dc:creator xml:lang="en">Purnamasari, Detty</dc:creator>
	<dc:creator xml:lang="en">Putra, Souza Nurafrianto Windiartono</dc:creator>
	<dc:subject xml:lang="en">Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en">Big Data</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Social</dc:subject>
	<dc:subject xml:lang="en">Network Analysis</dc:subject>
	<dc:description xml:lang="en">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 an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2024-09-09</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/436</dc:identifier>
	<dc:identifier>10.34306/att.v6i3.436</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 6 No. 3 (2024): November; 339−356</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 6 No 3 (2024): November; 339−356</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v6i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/436/279</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Nuryani, Achmad Benny Mutiara, I Made Wiryana, Detty Purnamasari, Souza Nurafrianto Windiartono Putra</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>