Electronic Devices, Methods, and Corresponding Systems for Precluding User Interaction Events in an Interactive Computing Environment

Abstract

An electronic device includes one or more processors, a user interface, and a memory. The device operates an electronic shopping interactive computing environment. Upon initiation of an interactive session, the processors determine a fraudulent return propensity score based on various input parameters, including device-level activities such as subscriber identification module card swaps, factory resets, and application reinstalls. When the propensity score exceeds a predefined threshold, the processors preclude one or more user interaction events, such as shopping cart interactions or product returns, from occurring in the shopping environment. The system can employ a machine learning algorithm to generate a normalized propensity score, enhancing fraud detection accuracy. This proactive approach mitigates the risk of fraudulent returns, protecting retailers from financial losses and maintaining the integrity of the e-commerce platform.

CPC Classifications