AI-Based Fraud Detection and Customer Protection in Online Markets: Balancing Transaction Security and Enhancing Digital Payment Adoption

 Author (s)

Abu Bakarr Koroma, Nabia Joanna Mansaray, Adiatu S. Kanu, & Matilda Khan

Abstract

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.

Keywords: Artificial Intelligence, Fraud Detection, Digital Payments, Customer Trust, Explainable AI (XAI).

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Cite This Article:

Koroma, A. B., Mansaray, N. J., Kanu, A. S., & Khan, M. (2026). AI-Based Fraud Detection and Customer Protection in Online Markets: Balancing Transaction Security and Enhancing Digital Payment Adoption, Journal of Market Research, 2(1), 44-64. DOI: https://doi.org/10.58970/JMR.5014

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About Author (s)

Abu Bakarr Koroma (Corresponding Author), Center for West African Studies (CWAS), University of Electronic Science and Technology of China (UESTC), Chengdu, China; School of Economics and Management (SEM) of UESTC, Chengdu, China, Ernest Bai Koroma University of Science and Technology (EBKUST), Sierra Leone, & Specializing Master & Lifelong Learning, Department of Energy, Politecnico di Milano, Milan, Italy. ORCID: https://orcid.org/0009-0005-3613-104X

Nabia Joanna Mansaray, Institute of Public Administration and Management (IPAM), University of Sierra Leone, Sierra Leone  &  School of Public Administration (SPA), University of Electronic Science and Technology of China (UESTC), Chengdu, China. ORCID: https://orcid.org/0009-0008-1611-5255

Adiatu S. Kanu, Postgraduate Studies, School of Social Sciences and Law, Njala University, Sierra Leone,  School of Postgraduate Studies, Department of Public Health, University of Makeni (UNIMAK), Sierra Leone, & District Health Management Team (DHMT), Bombali, Ministry of Health (MoH), Sierra Leone. ORCID: https://orcid.org/0009-0009-6719-5176

Matilda Khan, School of Public Administration (SPA), University of Electronic Science and Technology of China (UESTC), Chengdu, China & Department of Social Work, University of Sierra Leone, Sierra Leone. ORCID: https://orcid.org/0009-0001-7231-5218

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DOI: https://doi.org/10.58970/JMR.5014

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