Background: The surge in digital commerce and electronic payments has rapidly expanded the attack surface for fraudulent actors, prompting a corresponding acceleration in machine learning approaches to detect and mitigate online payment fraud (Keerthi & Nalini, 2024; Anitha et al., 2025). Simultaneously, scholarship in financial innovation and risk management emphasizes that fraud detection is not merely an engineering problem but a socio-technical challenge involving incentives, market design, and strategic interaction among buyers, sellers, platforms, and adversaries (Vanini et al., 2023; Seera et al., 2024).

Objective: This paper synthesizes empirical, methodological, and theoretical literatures to construct an integrative framework that links machine learning detection systems with game-theoretic incentive mechanisms and governance architectures for transactional integrity. The objective is to articulate design principles for detection systems that are resilient to strategic evasion, that internalize incentive misalignments, and that operate within practicable risk management regimes (Silva et al., 2022; Lucas & Jurgovsky, 2020).

Methods: The study undertakes a systematic synthesis of prior literature on supervised and ensemble machine learning methods for payment fraud detection (Abirami et al., 2018; Singh & Shukla, 2018; Silva et al., 2022), anomaly detection to risk management transition frameworks (Vanini et al., 2023), and incentive-based analyses from game theory applied to transactional integrity (Weiying, 1996; Zhang et al., 2007). The methodology is textual and conceptual: it combines methodological exposition of model families, threat modeling of adversarial behaviors, formalized incentive narratives from game-theoretic literature, and prescriptive governance recommendations.

Results: The integrative framework identifies three core adaptive mechanisms: (1) ensemble and hybrid model architectures to improve detection accuracy and reduce false positives while enabling model diversity against adversarial strategies (Silva et al., 2022; Seera et al., 2024); (2) dynamic risk-scoring systems coupled with economic incentives and reputational mechanisms to align actor behavior in marketplace settings (Zhang et al., 2007; Ma et al., 2005); and (3) governance and operational controls that transform anomaly alerts into enterprise-level risk responses, embedding human-in-the-loop decisioning and escalation pathways (Vanini et al., 2023; Singh, 2025). The framework further details countermeasures to adversarial evasion and discusses trade-offs between privacy, detection performance, and operational cost.

Conclusions: Robust online payment fraud detection requires more than optimized classifiers: it demands an integrated approach combining sophisticated machine learning ensembles, explicit game-theoretic incentive alignment, and institutional risk management strategies. Implementing such systems involves navigating technical, economic, and ethical trade-offs; policy, governance, and continuous monitoring are essential to sustain transactional integrity in evolving digital marketplaces.

online payment fraud, machine learning, ensemble methods, game theory

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