SYSTEM AND METHOD FOR DETECTING ABNORMAL ORDER PAYMENT BEHAVIOR USING GRAPH MODEL EMBEDDING AND ANOMALY DETECTION

Abstract

Some aspects of the present technology relate to technologies for detecting abnormal payment behavior using graph model embedding and anomaly detection. In accordance with some configurations, order payment data is collected from various sources, including e-commerce platforms, financial institutions, and payment processors. The collected payment data is structured as a graph for each order. Nodes represent individual payment transactions related to the order. Graph embedding techniques are applied to transform the payment data graph into a numerical vector space representation. The embedded data is analyzed for a particular interval of time to identify recurring patterns. A baseline for normal patterns is established for the interval of time and any patterns that deviate significantly from the baseline are flagged as potential abnormal payment behaviors. In some aspects, a graph visualization comparison tool aids in the transparent verification of reconciliations and provides intuitive insights for stakeholders.

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