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Basics of Multivariate Analysis in Neuroimaging Data
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A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A

Mianning Hu1, Xin Li1, Mingfeng Li1

  • 1School of Information and Network Security, People's Public Security University of China, Beijing 100038, China.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
Summary

This study introduces a Bayesian network model to predict telecom fraud losses. Findings show age impacts losses, anti-fraud campaigns reduce risk, and fraud peaks in summer and during sales events.

Keywords:
Bayesian networkearly warning frameworkmultiple heterogeneous datatelecom fraud

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Area of Science:

  • * Computer Science
  • * Criminology
  • * Data Science

Background:

  • * Telecommunication fraud poses a significant risk, necessitating advanced prevention and management strategies.
  • * Integrating multivariate heterogeneous data for front-end fraud detection remains a key research challenge.

Purpose of the Study:

  • * To develop a Bayesian network-based model for predicting telecom fraud risk and intervention effectiveness.
  • * To establish a telecom fraud analysis and warning framework using real-world data and expert knowledge.

Main Methods:

  • * A Bayesian network model was constructed using accumulated data, literature review, and expert insights.
  • * The model structure was refined using City S as a case study, incorporating telecom fraud mapping.
  • * Sensitivity analysis was performed to identify key risk factors and intervention impacts.

Main Results:

  • * Age demonstrates a maximum sensitivity of 13.5% concerning telecom fraud losses.
  • * Anti-fraud propaganda can decrease the likelihood of losses exceeding 300,000 yuan by 2%.
  • * Telecom fraud incidents are more prevalent in summer and less common in autumn, with notable increases during the Double 11 shopping period.

Conclusions:

  • * The developed model offers practical value for real-world telecom fraud prevention.
  • * The early warning framework provides crucial decision support for law enforcement and communities in identifying high-risk demographics, locations, and temporal patterns.
  • * Findings support targeted anti-fraud campaigns and timely warnings to mitigate financial losses.