Intelligent Behavioral Analytics for Cloud Payment Authorization
Keywords:
Payment As A Service, Cloud-Based Payment Platforms, Real-Time Transaction Authorization, Behavioral Analytics, Machine Learning–Powered Fraud Detection, Anomaly Detection Algorithms, High-Risk Payment Monitoring, Risk Sensing And Scoring, Behavioral Signal Enrichment, Transactional And Contextual Data, Supervised And Semi-Supervised Learning, Label-Free Inference Models, Money Laundering Prevention, Sensitive Transaction Protection, Risk Threshold Calibration, Authorization Decision Policies, Financial Crime Detection, Secure Payment Ecosystems, Intelligent Risk Management, Cloud-Native Payment Security.Abstract
Cloud-based platforms are increasingly incorporating Payment as a Service (PaaS) that empowers various organizations to swiftly offer payment services to their end users—most in a win-win manner. However, the growing violence and higher money flowing into crime severs the win-win situation and reduces the reception by the organizations. Payment transactions can never be completely anonymized. To keep the cloud-based transaction services flourish in the ecosystem, a technology that tracks criminals’ abnormal behaviors and predicts potential attacks before they happen is essential for sensitive, high-value, and risky transactions. Protecting the merchants and users by incorporating intelligent, machine learning (ML)-powered behavioral analytics for real-time authorization of sensitive transactions is a crucial and hot research topic; and their integrated receiving and sending decision policies should be taken care of in a balanced way.
To meet these needs and offer intelligent risk sensing and scoring services, the integration of denser behavioral signals in addition to the transactional and contextual ones is necessary. Various machine-learning-anomaly-detection algorithms are compared to select the most suitable model. Moreover, denser behavioral signals are searched and experimented with to attain ML models of higher accuracy. The normal classes are defined by both supervised learning for benign actions of authorized users and by semi-supervised clustering with a few labeled instances only for malicious actions of non-users. The models are label-free in the testing phase. Enriching high-risk monetary transfer authorization with intelligent decision-making is accomplished by ML-based risk scoring, where pleasing sensitivity and specificity are achieved through careful calibration of the risk scoring threshold.
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