A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

In the construction of a telecom-fraud risk warning and intervention-effect prediction model, how to apply multivariate heterogeneous data to the front-end prevention and management of telecommunication network fraud has become one of the focuses of this research. The Bayesian network-based fraud risk warning and intervention model was designed by taking into account existing data accumulation, the related literature, and expert knowledge. The initial structure of the model was improved by utilizing City S as an application example, and a telecom-fraud analysis and warning framework was proposed by incorporating telecom-fraud mapping. After the evaluation in this paper, the model shows that age has a maximum sensitivity of 13.5% to telecom-fraud losses; anti-fraud propaganda can reduce the probability of losses above 300,000 yuan by 2%; and the overall telecom-fraud losses show that more occur in the summer and less occur in the autumn, and that the Double 11 period and other special time points are prominent. The model in this paper has good application value in the real-world field, and the analysis of the early warning framework can provide decision support for the police and the community to identify the groups, locations, and spatial and temporal environments prone to fraud, to combat propaganda and provide a timely warning to stop losses.

Automated summary

In the research of constructing a telecom-fraud risk warning and intervention model, the focus is on utilizing multivariate heterogeneous data for front-end prevention and management of telecommunication network fraud. The Bayesian network-based model was designed by incorporating existing data, literature, and expert knowledge. The model was improved using City S as an example, and a telecom-fraud analysis and warning framework was proposed. The evaluation shows that age sensitivity to telecom-fraud losses is 13.5%, anti-fraud propaganda reduces the probability of losses above 300,000 yuan by 2%, and telecom-fraud losses are more prevalent in summer and less prevalent in autumn, with special time points like Double 11 being prominent. The model has practical application value and the warning framework provides decision support for identifying susceptible groups, locations, and temporal environments to combat fraud and prevent losses.