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Augment Legal Efforts through Artificial Intelligence in Curtailing Economic Fraud in Nigeria: Issues and Challenges

IJCLS (Indonesian Journal of Criminal Law Studies) · Vol. 10 No. 1 (2025) · pp. 115–148

DOI
10.15294/ijcls.v10i1.22147
ISSN
2548-1576, 2548-1568
Published
2025-05-15
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

The rise of economic fraud in Nigeria is one of the greatest threats to the financial stability of the nation, the governance systems, and socio-economic development. Though there are legal frameworks in curtailing economic fraud, there seem to be several challenges given the sophisticated economic frauds that have evolved. Therefore, this study aimed to examine AI as a transformative tool to supplement Nigeria's legal framework in its legal fight against economic fraud through facilitating the improvement of violation detection, automation of evidence gathering, and support of data-driven policymaking. It also enhances efficiency in law enforcement via predictive analytics and real-time monitoring systems. The study adopts a qualitative methodology, including an analysis of the legal framework and a literature review on AI in combating economic fraud. The findings show that despite some legal frameworks put in place for economic fraud, the legal mechanisms for the detection, prevention, and prosecution of economic fraud lack adequate capacity and nearly totally lack a procedural perspective. Also, in the study, it was found that AI technologies such as machine learning and natural language processing can help in providing means for those datasets to find some patterns, improving forensic investigations and decision-making processes in fraud cases. Thus, it stipulates that integrating AI into Nigeria's Legal Framework is essential for combating economic fraud. Policy reforms, capacity-building programs, and investment in AI infrastructure have been recommended to facilitate its adoption.

Keywords

Collaboration network of the authors

30 authors · 79 co-authorship links

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Co-authorOrganisationJoint articles
Lindra DarnelaAffiliation unknown4
Souad EzzeroualiAffiliation unknown3
Esther Chetachukwu AidonojieAffiliation unknown2
Oaihimire Idemudia EdetalehnAffiliation unknown2
Oluwaseye Oluwayomi IkubanniAffiliation unknown2
Alade Adeniyi OyebadeAffiliation unknown2
Mukhlish MukhlishAffiliation unknown2
Mercy Osemudiame OkpokoAffiliation unknown2
Eregbuonye ObieshiAffiliation unknown2
Mukhlis MukhlisAffiliation unknown2
Evis GarunjaAffiliation unknown2
Raphael D. Jackson-OrtizAffiliation unknown2
Ousu MendyAffiliation unknown2
Ebrima SarrAffiliation unknown2
Raphael Daoud Jackson-OrtizAffiliation unknown2
Yusuf Abass AleshinloyeAffiliation unknown1
Ogoma Sunday AniAffiliation unknown1
Obieshi EregbuonyeAffiliation unknown1
Ismaila HassanAffiliation unknown1
. PamujiAffiliation unknown1
M. IqbalAffiliation unknown1
Ajeng Permata SariAffiliation unknown1
FernandhoAffiliation unknown1
Nurus ZamanAffiliation unknown1
Uswatun HasanahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ijcls/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (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">Augment Legal Efforts through Artificial Intelligence in Curtailing Economic Fraud in Nigeria: Issues and Challenges</dc:title>
	<dc:creator xml:lang="en">Erma Rusdiana</dc:creator>
	<dc:creator xml:lang="en">Paul Atagamen Aidonojie</dc:creator>
	<dc:creator xml:lang="en">Muwaffiq Jufri</dc:creator>
	<dc:creator xml:lang="en">Hassan Ismaila</dc:creator>
	<dc:creator xml:lang="en">Nathan Ibrahim</dc:creator>
	<dc:subject xml:lang="en">Artificial intelligence</dc:subject>
	<dc:subject xml:lang="en">Economic fraud</dc:subject>
	<dc:subject xml:lang="en">Economic crime</dc:subject>
	<dc:subject xml:lang="en">Nigeria</dc:subject>
	<dc:description xml:lang="en">The rise of economic fraud in Nigeria is one of the greatest threats to the financial stability of the nation, the governance systems, and socio-economic development. Though there are legal frameworks in curtailing economic fraud, there seem to be several challenges given the sophisticated economic frauds that have evolved. Therefore, this study aimed to examine AI as a transformative tool to supplement Nigeria's legal framework in its legal fight against economic fraud through facilitating the improvement of violation detection, automation of evidence gathering, and support of data-driven policymaking. It also enhances efficiency in law enforcement via predictive analytics and real-time monitoring systems. The study adopts a qualitative methodology, including an analysis of the legal framework and a literature review on AI in combating economic fraud. The findings show that despite some legal frameworks put in place for economic fraud, the legal mechanisms for the detection, prevention, and prosecution of economic fraud lack adequate capacity and nearly totally lack a procedural perspective. Also, in the study, it was found that AI technologies such as machine learning and natural language processing can help in providing means for those datasets to find some patterns, improving forensic investigations and decision-making processes in fraud cases. Thus, it stipulates that integrating AI into Nigeria's Legal Framework is essential for combating economic fraud. Policy reforms, capacity-building programs, and investment in AI infrastructure have been recommended to facilitate its adoption.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-05-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ijcls/article/view/22147</dc:identifier>
	<dc:identifier>10.15294/ijcls.v10i1.22147</dc:identifier>
	<dc:source xml:lang="en">IJCLS (Indonesian Journal of Criminal Law Studies); Vol. 10 No. 1 (2025): Indonesia J. Crim. L. Studies (May, 2025); 115-148</dc:source>
	<dc:source>2548-1576</dc:source>
	<dc:source>2548-1568</dc:source>
	<dc:source>10.15294/ijcls.v10i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ijcls/article/view/22147/4010</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Indonesian Journal of Criminal Law Studies</dc:rights>
</oai_dc:dc>