With the development of mobile payment and other online shopping and service platforms in recent years, so too have frauds in these areas been on the rise. The old way of risk control is too simple and cannot recognize some fraudulent behaviour that may be disguised as normal activity. Joint modelling of user behaviour and terminal device trustworthiness in consumer technology scenarios. First, a general risk feature system is built based on user behaviour, transaction process, device status, network environment and historical risk data. The IEEE-CIS Fraud Detection Dataset provided by Kaggle will serve as the public empirical data. Based on the above data, features of transaction behaviour, payment association, identity verification and device attributes are extracted from several models and compared. The external validation results show that the joint feature model achieves the highest ROC-AUC and PR-AUC, indicating that device association and operation-source information can improve fraud-risk ranking. It can be seen from the above that device and identity-related information can be added to the features of transaction behaviour to improve fraud detection. The study suggests that intelligent risk control should evaluate both user operations and operation source trustworthiness, so as to support more accurate fraud identification and tiered control strategies.

Zhaoning Zhang. User Behavior Device Reliability Joint Modeling and Intelligent Risk Control Practice In Consumer Technology. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 2: 61-72. https://doi.org/10.38007/IJBDIT.2026.070207.

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