Resilient Detection of Credit Card Fraud in Digital Marketplaces: An Integrated Behavioral and Machine Learning Framework for Transactional Integrity

Abstract

Background: The increasing digitization of commerce, coupled with the expansion of online marketplaces and the sophistication of financial technologies, has amplified vulnerabilities in transactional systems, particularly credit card fraud (Dellarocas, 2003; Internet Development Report, 2012). Existing detection systems vary widely — from classical supervised learning models to hybrid deep learning architectures — yet significant gaps remain in capturing behavioral anomalies, addressing data asymmetries, and providing resilient, operationally deployable solutions (Shirgave et al., 2019; Parthiban et al., 2021).

Objective: This article proposes and elaborates a theoretically grounded, publication-ready framework that synthesizes behavioral signals with advanced machine learning methods to improve detection accuracy, reduce false positives, and strengthen transactional integrity within online payment ecosystems. The work draws strictly from the supplied literature to map current methods, limitations, and opportunities for integrated approaches.

Methods: We undertake a comprehensive conceptual-methodological synthesis of supervised learning approaches, hybrid deep models, and behavioral analytics. Building on empirical and theoretical insights from the literature, our methodological design details feature engineering focused on behavioral indicators, risk-scoring paradigms, semi-supervised augmentation strategies, and layered system architectures for deployment (Thakur & Gudadhe, 2021; Wang, 2020; Sharma et al., 2024).

Results: The theoretical application of this integrated framework predicts substantial improvements in detection sensitivity and specificity when behavioral features are systematically combined with hybrid deep learning ensembles and classical supervised classifiers. The model addresses common operational constraints — data imbalance, concept drift, and adversarial behavior — through explicit procedures including synthetic minority augmentation, sliding-window retraining, and behaviorally informed alert thresholds (Parthiban et al., 2021; Sharma et al., 2024).

Conclusion: Integrating behavioral analytics with machine learning — implemented as a layered, adaptive architecture — offers a promising path toward robust fraud detection and transactional security in digital marketplaces. The framework balances detection performance with operational feasibility and suggests targeted avenues for empirical validation, dataset construction, and cross-disciplinary policy design. Limitations include the theoretical nature of the present work and the need for real-world trials under diverse market conditions (Shirgave et al., 2019; Singh, 2025).