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Artificial-intelligence-integrated 'buy now, pay later' (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice-architecture, payment-decoupling, and financial-literacy literatures, this study examines how three platform-level features — algorithmic nudging, AI personalization intensity, and perceived ease of credit — are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial-least-squares structural-equation modeling (N = 1,247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. AI personalization was positively associated with platform loyalty but co-varied with indirect indicators of financial risk — a pattern we describe as a loyalty trap and empirically document via piecewise longitudinal trajectories. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.