The invention discloses an intelligent detection and interception method and
system for an abnormal rebate behavior in a recruitment scene, belongs to the technical field of
artificial intelligence and risk management and control, and aims to solve the technical problems that existing recruitment rebate
anomaly detection depends on an artificial rule, model results are difficult to fuse, and an interception strategy is rigid. The method comprises the steps of obtaining a user behavior sequence and associated
context data in a recruitment and rebate scene, constructing a multi-
modal feature set, carrying out multi-model
parallel detection to obtain an abnormal probability, calculating an initial confidence coefficient and a dynamic weight of a model, generating a comprehensive abnormal
score, triggering a grading interception strategy, and incrementally updating the model and an
arbiter. Meanwhile, a
system for implementing the method is constructed and comprises a
data acquisition module, a multi-
modal feature construction module, a multi-
model selection detection module, a confidence coefficient calculation module, a dynamic arbitration module, a hierarchical interception module and a feedback updating module, and all the modules collaboratively achieve multi-model linkage, dynamic arbitration and closed-
loop optimization. According to the method, the recognition accuracy of abnormal behaviors such as scalping and post counterfeiting is improved, different
risk control requirements are met, data privacy is guaranteed, dual
risk control protection of the method and the
system is realized, and the prevention and control efficiency of recruitment and rebate scenes is remarkably improved.