Abstract
The present disclosure relates to a system and method for managing e-commerce return and refund risks by utilizing a comprehensive data aggregation module that collects information metrics from multiple e-commerce platforms using a unique identifier. The user's purchase profile is generated through a user profiling component leveraging Visa ID to link all active identification identifiers. A risk assessment engine computes various risk indicators based on historical purchasing behaviors, including sales-to-refund ratios and fraud signals. Furthermore, the system includes a dashboard interface that presents comparative metrics, allowing merchants to analyze consumer behavior against a broader consumer base. This structured approach facilitates accurate predictions of potential losses associated with returns and refunds, ultimately enhancing decision-making for merchants.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ahuja, Ishika; K M, Hema Lakshmi; Kesavamurthy, Kireeti; and Gandhi, Bhuvanjeet Singh, "A METHOD AND A SYSTEM FOR PREDICTING E-COMMERCE CONSUMER RETURN RISKS AND BEHAVIOR ANALYSIS", Technical Disclosure Commons, (September 26, 2025)
https://www.tdcommons.org/dpubs_series/8642