Calorine Katushabe

Fraud in Savings and Credit Cooperative Organizations (SACCOs) remains a major challenge that undermines financial inclusion and sustainability in developing countries. This study conducted a systematic literature review to examine both traditional and emerging fraud patterns and evaluate fraud detection methods with emphasis on artificial intelligence and machine learning applications. A comprehensive structured search across Web of Science, Scopus, and Google Scholar yielded 28 peer-reviewed studies published between 2015 and 2025 that met eligibility and quality criteria. The findings reveal that traditional fraud patterns such as member collusion, embezzlement, and asset misappropriation coexist with emerging digital fraud such as mobile payment fraud, phishing, card fraud, and cryptocurrency scams. While rule-based and audit-based detection remain ineffective, machine learning has demonstrated significant promise for real-time detection but faces challenges related to class imbalance, interpretability, and data privacy. The review identified a weak Information and Communication Technology (ICT) infrastructure, the absence of SACCO-specific fraud detection models, and hybrid frameworks. It concludes that hybrid models that integrate traditional audit methods with machine learning are recommended for SACCO-specific fraud detection frameworks. This study emphasizes the need for future research on explainable AI and privacy-preserving analytics to enhance fraud resilience in SACCOs.

The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.