Data-Driven Bayesian Network for Risk Analysis of Telecom Fraud

Abstract

Given the widespread occurrence of global telecom fraud, the development of proactive measures for crime prevention and control has become increasingly crucial. This study introduces a data-driven Bayesian Network (BN) model, which incorporates D-S evidence theory to integrate prior knowledge for fraud risk analysis. Through the examination of real-world case data, the study identifies key risk-influencing factors (RIFs) and uncovers causal relationships by comparatively evaluating three structure learning algorithms: Peter-Clark (PC), Bayesian Search (BS), and Greedy Thick Thinning (GTT). A robust Directed Acyclic Graph (DAG) is then constructed, and the Expectation-Maximization (EM) algorithm is employed to estimate conditional probability distributions. The proposed model effectively captures the causal relationships and nonlinear complexities among RIFs. To validate the model's applicability, scenario reasoning and sensitivity analysis are conducted, confirming its effectiveness in prioritizing RIFs and supporting informed decision-making. This research presents a novel and practical framework for public security agencies to develop proactive strategies for telecom fraud prevention and control.