AI Fraud Detection and Financial Trust in Nepal’s SME Payment Ecosystem: A Readiness Framework

Mapping the Intellectual Landscape of AI-powered Financial Fraud Detection: Insights from Bibliometric and Thematic Analysis

This study offers an evidence-based account of how AI in fraud detection has evolved and proposes a future research agenda emphasizing transparency, ethical assurance and global governance alignment, advancing financial risk management through conceptual clarity, methodological guidance and actionable pathways for responsible AI adoption.

AI-Driven Fraud Detection in Financial Systems: A CRM-Centric Approach

This paper describes a system for the detection of fraud, which is both dynamic and adaptable and which is obtained through the synthesis of machine learning techniques and the CRM data streams and shows how this unified method can lead to an increase in detection performance, shortening of the time for the response, and higher customer confidence in comparison to the existing systems.

AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative TOE-UTAUT Model for Accounting Transformation

This manuscript develops a quantitative research model for examining artificial intelligence (AI)-enabled fraud detection readiness among small and medium-sized enterprises (SMEs) in Sarawak, Malaysia. Rather than treating AI adoption solely as a general technology acceptance issue, the paper positions adoption readiness as a multi-layered condition shaped by technological fit, organizational mobilisation, institutional facilitation, and user-level acceptance. Drawing on the Technology-Organization-Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model explains how relative advantage, compatibility, cost, security, task-technology fit, business innovativeness, top management support, government support, effort expectancy, and social influence may affect SMEs' intention to adopt AI in accounting. Trust and firm size are incorporated as boundary conditions to capture behavioral uncertainty and resource asymmetry in regional SME settings. The manuscript further specifies a survey-based methodological protocol involving purposive sampling, a seven-point Likert-scale questionnaire, pilot testing, SPSS-based data screening, and Partial Least Squares Structural Equation Modelling (PLSSEM) using SmartPLS. By emphasizing readiness, measurement logic, and empirical testability, the paper offers a methodological contribution to AI accounting research and provides a replicable model for studying digitally underserved regional economies.

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

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.

AI-Driven Anomaly Detection in U.S. University Networks: A Qualitative Study of Development, Implementation, and Governance

Despite the growing adoption of artificial intelligence (AI) to strengthen cybersecurity, qualitative research examining the development, implementation, and governance of AI-driven anomaly detection systems within U.S. higher education remains limited. Universities present a distinct cybersecurity environment due to their decentralized IT infrastructures, diverse stakeholder communities, and the need to balance security with academic openness.This study investigates the development and implementation of AI-driven anomaly detection systems for identifying abnormal network behavior in U.S. university environments. A qualitative multiple-case study design was employed, drawing on semi-structured interviews with 11 cybersecurity professionals from six U.S. universities. The interview data were analyzed using thematic analysis to address the study's research questions. The findings reveal that the adoption and operation of AI-driven anomaly detection systems are shaped by several interconnected factors, including vendor influence, governance frameworks, and ethical considerations. Participants also identified significant implementation challenges, such as false-positive alerts, the complexity of university network environments, and institutional cultures that emphasize academic freedom and privacy. Overall, the findings highlight the critical role of effective governance in ensuring the successful deployment and responsible use of AI-driven cybersecurity technologies in higher education.