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    AI-Based Fraud Detection and Customer Protection in Online Markets: Balancing Transaction Security and Enhancing Digital Payment Adoption
    (IJSAB International, 2026) BAKARR, KOROMA ABU; BAKARR, KOROMA ABU
    The rapid global adoption of digital payments has precipitated an escalating arms race against sophisticated financial fraud, creating a critical tension between implementing robust security measures and maintaining a frictionless user experience that fosters acceptance. Advanced, current, AI-driven fraud detection systems often operate in silos, lack adaptive mechanisms against evolving threats, and fail to explicitly optimise for customer-centric outcomes like trust and satisfaction. To fill these gaps, this study proposes, develops, and validates the Adaptive Security-Trust Equilibrium (ASTE) Framework, a new AI system that aims to find a balance between security and the use of digital payments. The framework synergises a multi-layered transaction risk engine (utilising an extended isolation forest and a stacked ensemble of LightGBM and DNN classifiers), a temporal graph neural network for detecting collusive fraud rings, adaptive behavioural biometrics for continuous authentication, and an Explainable AI (XAI) interface for transparency. Validated on a large-scale amalgamated dataset of over 1.5 million transactions (3.2% fraud rate) synthesised from public and synthetic sources, the ASTE Framework demonstrated superior performance. It achieved a fraud detection F₂ score of 0.891 (recall: 0.924) while reducing the false positive rate to 0.8%, which translated to a 52% reduction in the customer friction index compared to a random forest baseline. The framework extended prior models by formally integrating graph-based relational analysis and a dynamic user trust score into a meta-fusion decision layer. Robustness checks confirmed stable performance under temporal drift and noisy data conditions. Simulations indicated a projected 22.4% increase in median user trust scores and a strong positive correlation (\*r\* = 0.71) between XAI explanations and user confidence. The findings confirm that a holistic, adaptive, and explainable AI architecture can successfully reconcile the security-adoption paradox, offering a viable path for payment providers to enhance protection, improve customer experience, and support secure financial inclusion.