Name
Abstract | ||
|---|---|---|
Fraud detection in the telecommunications industry requires the identification of a small fraction of fraudulent calls from the high volume of call traffic. This presents a significant research challenge in the design of efficient and effective algorithms to combat telecommunications fraud. This paper employs Latent Dirichlet Allocation (LDA) to build user profile signatures and assumes that any significant unexplainable deviations from the normal activity of an individual user is strongly correlated with fraudulent activity. The user activity is represented as a probability distribution over call features which surmises the user's calling behaviour. This probability distribution is derived from LDA which can accurately describe user profiles by combining different classes of distributions. To score calls we compare the likelihood of the user generating a call versus a fraudster generating the same call. Our experiments demonstrate that using such a probability distribution and employing even a rough profile of a fraudster's activity ameliorates the detection of fraudulent calls. Our method is computationally efficient and can scale up to a realistic real time detection. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.patrec.2007.04.015 | Pattern Recognition Letters |
Keywords | Field | DocType |
data mining,user profile,call traffic,user modelling,fraud detection,telecommunications,employing latent dirichlet allocation,fraudulent call,call feature,user profile signature,fraudulent activity,latent dirichlet allocation,user activity,individual user,probability distribution,normal activity,real time | Data mining,Latent Dirichlet allocation,Data processing,User profile,Telecommunications,Dirichlet problem,Computer science,Probability distribution,Linear discriminant analysis | Journal |
Volume | Issue | ISSN |
28 | 13 | Pattern Recognition Letters |
Citations | PageRank | References |
37 | 1.39 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongshan Xing | 1 | 101 | 5.31 |
| Mark Girolami | 2 | 1382 | 141.16 |