A Real-Time Fraud Detection Algorithm Based on Intelligent Scoring for the Telecom Industry

Fraud detection is one of the biggest challenges in the telecom industry. Commonly used approaches, such as rule sets, outlier detection, and classification, have high computational cost, so they don't work well on mass data in terms of accuracy and speed. Besides, those algorithms are not good at detecting new fraud patterns. In this paper, we propose an UIS (United Intelligent Scoring) algorithm for fraud detection which has three merits. First, it has lower computational complexity. We use Manhattan distance instead of Euclidean distance to measure similarity between fraud samples and ordinaries. Second, new fraud patterns can be detected effectively by joint fraud probability. Finally, UIS is able to generate and update real-time scores, which detects early-time fraud and minimizes economic losses. Integrated experiments on real datasets of the telecom industry demonstrate that UIS is real-time, effective, and robust in different situations.

[1]  Paris A. Mastorocostas,et al.  An application of supervised and unsupervised learning approaches to telecommunications fraud detection , 2008, Knowl. Based Syst..

[2]  Ronnie Alves,et al.  Discovering telecom fraud situations through mining anomalous behavior patterns , 2006 .

[3]  Daya Gupta,et al.  An Analysis of Telecommunication Fraud using Outlier Detection Model based on Similar Coefficient Sum , 2014 .

[4]  Sharmila Subudhi,et al.  Quarter-Sphere Support Vector Machine for Fraud Detection in Mobile Telecommunication Networks , 2015 .

[5]  Chang-Tien Lu,et al.  Survey of fraud detection techniques , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[6]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[7]  John Shawe-Taylor,et al.  An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection , 2001, J. Parallel Distributed Comput..

[8]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[9]  Yizhak Idan,et al.  Discovery of fraud rules for telecommunications—challenges and solutions , 1999, KDD '99.

[10]  Joos Vandewalle,et al.  Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype , 1997, ICANN.

[11]  Jaakko Hollmén,et al.  User profiling and classification for fraud detection in mobile communications networks , 2000 .

[12]  Hongzhi Yu,et al.  ID3 Decision Tree in Fraud Detection Application , 2012, 2012 International Conference on Computer Science and Electronics Engineering.