Risk Analytics Framework for Monitoring FinTech Transactions in Emerging Markets: Evidence from Puntland, Somalia

Puntland, Somalia, has developed into one of the most mobile-money-intensive economies in Sub-Saharan Africa, mostly propelled by telecommunications-driven financial services rather than conventional banking institutions. This digital shift has markedly improved financial inclusion but has concurrently added intricate hazards associated with fraud, money laundering, terrorist financing, and cybercrime. This report offers a PRISMA-compliant systematic analysis of risk analytics frameworks for overseeing FinTech transactions in emerging markets, specifically applied to Puntland's mobile money ecosystem. A total of 36 studies published from 2016 to 2025 were examined, encompassing mobile money operators such as Golis (Sahal), Somtel (Edahab), Amtel (MyCash), and integrated digital platforms including Waafi and Taaj. The results demonstrate that sophisticated data-driven methodologies-specifically ensemble machine learning models such as Gradient Boosted Decision Trees and Random Forests, in conjunction with statistical volatility models like GARCH(1,1)-consistently surpass conventional rule-based monitoring systems in identifying anomalous transactions. Nonetheless, performance indicators exhibit significant variability between datasets and jurisdictions, highlighting the imperative for localized calibration and control. The research underscores the pivotal function of Somalia's National Identification and Registration Authority (NIRA), biometric Know Your Customer protocols, and the Financial Reporting Centre's goAML platform in enhancing compliance with Anti-Money Laundering and Countering the Financing of Terrorism regulations. A comprehensive, multi-tiered risk analytics methodology is offered to enhance financial integrity, regulatory efficacy, and sustainable digital economic development in post-conflict emerging countries.