| 摘要 |
A computerized method is established to detect suspicious and fraudulent activities in a group of subjects by defining and dynamically integrating multidimensional risks, which are based on the characteristics of the subjects, into a mathematical model to produce a set of the most up-to-date representative risk values for each subject based on its activities and background. These multidimensional risk definitions and representative risk values are used to select a subset of multidimensional risk-weighted detection algorithms so that suspicious or fraudulent activities in the group of subjects can be effectively detected with higher resolution and accuracy. A priority sequence, which is based on the set of detection algorithms that detect the subject and the representative risk values of the detected subject, is produced to determine the priority of each detected case during the investigation process. To assist the user to make a more objective decision, any set of multidimensional risks can be used to identify a group of subjects that contain this set of multidimensional risks so that group statistics can be obtained for comparison and other analytical purposes. Furthermore, to fine-tune the system for future detections and analyses, the detection results are used as the feedback to adjust the definitions of the multidimensional risks and their values, the mathematical model, and the multidimensional risk-weighted detection algorithms. |
