Cybersecurity in FinTech: A Machine Learning-Based Framework for Threat Detection in Mobile Payments

The rapid evolution of Financial Technology (FinTech) has revolutionized mobile payment systems, offering seamless, efficient, and real-time financial services. However, this digital transformation has simultaneously introduced complex cybersecurity challenges, particularly as cybercriminals increasingly exploit mobile platforms. This study proposes a novel machine learning-based framework for proactive threat detection in mobile payment environments, integrating behavioral analytics, device fingerprinting, and network anomaly detection. The framework leverages supervised and unsupervised learning models—such as Random Forest, Isolation Forest, and Autoencoders—to identify both known and zero-day threats with high precision. A hybrid feature engineering pipeline is also introduced, combining static application metadata with dynamic transaction behavior to enhance detection accuracy. Experimental results on real-world mobile payment datasets demonstrate that the proposed framework achieves superior performance in terms of precision, recall, and F1-score compared to traditional signature-based and rule-based detection systems. This research contributes to the advancement of secure FinTech ecosystems by offering a scalable and adaptive solution for real-time cyber threat mitigation in mobile payments.