Fraud Detection Life Cycle Model: A Systematic Fuzzy Approach to Fraud Management

Advancements in science and technology have impacted the global scenario significantly in each and every sphere of life. Unfortunately, this has also caused an increase in the number of frauds in various fields. Fraudsters are making an illegal access to the users' account in parallel with the users without being detected which results in heavy losses in terms of money, data and time. Therefore, detection of frauds has become an important need for the organizations not only to prevent the misuse but also to detect and report any such access as and when it is made. In this paper, a fraud detection life cycle model is proposed which reveals the detection of fraudulent behavior of customers. The objective of this life cycle model is to minimize the frauds occurring in different areas which are sensitive to fraudulent behavior such as telecommunication, credit card, finance industry and so on. We have presented a case study of Public Sector Telecommunication Company to demonstrate the life cycle model and further shown how our proposed life cycle model works on it by using some fuzzy-based inference rules for efficient detection of frauds.

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

[2]  Constantinos S. Hilas,et al.  Designing an expert system for fraud detection in private telecommunications networks , 2009, Expert Syst. Appl..

[3]  N. Delavari,et al.  A new model for using data mining technology in higher educational systems , 2004, Information Technology Based Proceedings of the FIfth International Conference onHigher Education and Training, 2004. ITHET 2004..

[4]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[5]  Douglas L. Reilly,et al.  Credit card fraud detection with a neural-network , 1994, 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences.

[6]  Melody Y. Kiang,et al.  The effect of sample size on the extended self-organizing map network - A market segmentation application , 2007, Comput. Stat. Data Anal..

[7]  M Syeda,et al.  Parallel granular neural networks for fast credit card fraud detection , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

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

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Jiuyong Li Robust rule-based prediction , 2006, IEEE Transactions on Knowledge and Data Engineering.

[11]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[12]  Erhard Rahm,et al.  Data Cleaning: Problems and Current Approaches , 2000, IEEE Data Eng. Bull..

[13]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[14]  Steven Salzberg,et al.  A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features , 2004, Machine Learning.

[15]  Jay F. Nunamaker,et al.  Information Systems : decision support and knowledge-based systems , 1994 .

[16]  Joos Vandewalle,et al.  Fraud detection in mobile communications using supervised neural networks , 1997 .

[17]  Mohammad Mehdi Sepehri,et al.  A data mining framework for detecting subscription fraud in telecommunication , 2011, Eng. Appl. Artif. Intell..

[18]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[19]  Gupta Daya,et al.  A Novel Framework and Model for Data Warehouse Cleansing , 2011 .

[20]  Tom Fawcett,et al.  Combining Data Mining and Machine Learning for Effective User Profiling , 1996, KDD.

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

[22]  Dominik Olszewski,et al.  A probabilistic approach to fraud detection in telecommunications , 2012, Knowl. Based Syst..

[23]  Wang Da-zhen,et al.  Fraud Detection in Mobile Communication Networks , 2004 .

[24]  John Shawe-Taylor,et al.  Frameworks For Fraud Detection In Mobile Telecommunications Networks , 1996 .