Artificial Intelligence Techniques and Cryptocurrency Fraud Detection in Kenya: A Systematic Literature Review Using the PRISMA Framework

Charles G. Kamau
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Charles G. Kamau: Technical University of Mombasa

African Multidisciplinary Scholarship Journal, 2026, vol. 1, issue 1

Abstract: The increasing adoption of cryptocurrencies has created new opportunities for digital financial innovation while simultaneously exposing individuals and institutions to sophisticated forms of financial fraud. Conventional rule-based fraud detection systems have become inadequate in addressing the dynamic and complex nature of blockchain-enabled financial crimes, leading to growing interest in the application of artificial intelligence (AI). This study systematically reviews the literature on artificial intelligence techniques for cryptocurrency fraud detection, with particular emphasis on their relevance to the Kenyan digital financial ecosystem. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Peer-reviewed studies published between 2020 and 2026 were identified from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar. Following the screening and eligibility assessment, 19 studies were included in the final qualitative synthesis. The findings reveal that machine learning, deep learning, hybrid AI models, and blockchain analytics significantly enhance cryptocurrency fraud detection by improving anomaly detection, transaction monitoring, predictive accuracy, and anti-money laundering compliance. Compared with traditional rule-based approaches, AI techniques provide faster, more adaptive, and scalable solutions capable of detecting evolving fraud patterns in decentralized financial systems. However, the review also identifies challenges relating to limited high-quality datasets, algorithmic bias, lack of explainability, cybersecurity risks, privacy concerns, and inadequate regulatory frameworks, particularly within developing economies. Furthermore, the review highlights a scarcity of empirical research focusing on cryptocurrency fraud detection in Kenya and identifies opportunities for developing localized datasets, explainable AI models, and context-specific regulatory frameworks. The study concludes that artificial intelligence has considerable potential to strengthen cryptocurrency fraud detection and financial security in Kenya, provided that technological, ethical, and regulatory challenges are adequately addressed. The findings provide valuable insights for researchers, financial institutions, technology developers, and policymakers seeking to enhance AI-driven fraud prevention within the country's evolving digital financial ecosystem.

Keywords: Artificial Intelligence; Cryptocurrency Fraud Detection; Machine Learning; Blockchain Analytics; Kenya (search for similar items in EconPapers)
JEL-codes: C88 G23 G28 O33 (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cwk:amsjke:2026-06

DOI: 10.59413/amsj/v1.i1.6

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