GOTCHA! Network-Based Fraud Detection for Social Security Fraud

References

• Akoglu L, Chandy R, Faloutsos C (2013) Opinion fraud detection in online reviews by network effects. Proc. Seventh Internat. AAAI Conf. Weblogs and Soc. Media (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 1–11.Google Scholar
• Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: Spotting anomalies in weighted graphs. Advances Knowledge Discovery and Data Mining (Springer, Berlin), 410–421.Crossref, Google Scholar
• Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. USA 106(51):21544–21549.Crossref, Google Scholar
• Baesens B, Setiono R, Mues C, Vanthienen J (2003a) Using neural network rule extraction and decision tables for credit-risk evaluation. Management Sci. 49(3):312–329.Link, Google Scholar
• Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J, Vanthienen J (2003b) Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6):627–635.Crossref, Google Scholar
• Bapna R, Umyarov A (2015) Do your online friends make you pay? A randomized field experiment on peer influence in online social networks. Management Sci. 61(8):1902–1920.Link, Google Scholar
• Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: Structure and dynamics. Physics Reports 424(4):175–308.Crossref, Google Scholar
• Bolton RJ, Hand DJ (2002) Statistical fraud detection: A review. Statist. Sci. 17(3):235–249.Crossref, Google Scholar
• Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.Crossref, Google Scholar
• Carrizosa E, Martín-Barragán B, Romero Morales D (2014) A nested heuristic for parameter tuning in support vector machines. Comput. Oper. Res. 43(March):328–334.Crossref, Google Scholar
• Chan PK, Stolfo SJ (1998) Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. Proc. Fourth Internat. Conf. Knowledge Discovery and Data Mining (AAAI Press, Menlo Park, CA), 164–168.Google Scholar
• Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: A survey. ACM Comput. Surveys 41(3):Article 15.Crossref, Google Scholar
• Chau DH, Pandit S, Faloutsos C (2006) Detecting fraudulent personalities in networks of online auctioneers. Proc. Tenth Eur. Conf. Principles and Practice of Knowledge Discovery in Databases (Springer, Berlin), 103–114.Crossref, Google Scholar
• Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2011) SMOTE: Synthetic minority over-sampling technique. J. Artificial Intelligence Res. 16:321–357.Crossref, Google Scholar
• Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: A general framework and some examples. Computer 37(4):50–56.Crossref, Google Scholar
• Chiu C, Ku Y, Lie T, Chen Y (2011) Internet auction fraud detection using social network analysis and classification tree approaches. Internat. J. Electronic Commerce 15(3):123–147.Crossref, Google Scholar
• Cortes C, Pregibon D, Volinsky C (2001) Communities of interest. Proc. Fourth Internat. Conf. Adv. Intelligent Data Anal. (Springer, Berlin), 105–114.Crossref, Google Scholar
• Dionne G, Giuliano F, Picard P (2009) Optimal auditing with scoring: Theory and application to insurance fraud. Management Sci. 55(1):58–70.Link, Google Scholar
• Easley D, Kleinberg J (2010) Networks, Crowds, and Markets (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Eliassi-Rad T, Henderson K (2011) Ranking information in networks. Social Computing, Behavioral-Cultural Modeling and Prediction (Springer, Berlin), 268–275.Crossref, Google Scholar
• Estévez PA, Held CM, Perez CA (2006) Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems Appl. 31(2):337–344.Crossref, Google Scholar
• Fast A, Friedland L, Maier M, Taylor B, Jensen D, Goldberg HG, Komoroske J (2007) Relational data pre-processing techniques for improved securities fraud detection. Proc. Thirteenth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (Association for Computing Machinery, New York),941–949.Crossref, Google Scholar
• Fawcett T, Provost F (1997) Adaptive fraud detection. Data Mining Knowledge Discovery 1(3):291–316.Crossref, Google Scholar
• Furlan Š, Bajec M (2008) Holistic approach to fraud management in health insurance. J. Inform. Organ. Sci. 32(2):99–114.Google Scholar
• Gallagher B, Tong H, Eliassi-Rad T, Faloutsos C (2008) Using ghost edges for classification in sparsely labeled networks. Proc. Fourteenth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 256–264.Crossref, Google Scholar
• Galloway J, Simoff SJ (2006) Network data mining: Discovering patterns of interaction between attributes. Advances in Knowledge Discovery and Data Mining (Springer, Berlin), 410–414.Crossref, Google Scholar
• Gao Z, Ye M (2007) A framework for data mining-based anti-money laundering research. J. Money Laundering Control 10(2):170–179.Crossref, Google Scholar
• Gleich DF (2015) PageRank beyond the web. SIAM Rev. 57:321–363.Crossref, Google Scholar
• Goldberg HG, Senator TE (1995) Restructuring databases for knowledge discovery by consolidation and link formation. Proc. 1st Internat. Conf. on Knowledge Discovery and Data Mining (AAAI Press, Palo Alto, CA), 136–141.Google Scholar
• Gyöngyi Z, Garcia-Molina H, Pedersen J (2004) Combating web spam with TrustRank. Proc. 30th Internat. Conf. Very Large Databases (VLDB Endowment, Toronto), 576–587.Google Scholar
• Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning, Vol. 1 (Springer, New York).Crossref, Google Scholar
• Hilas CS, Sahalos JN (2005) User profiling for fraud detection in telecommunication networks. Proc. Fifth Internat. Conf. Tech. Automation (IEEE, Piscataway, NJ), 382–387.Google Scholar
• Jensen D (1997) Prospective assessment of AI technologies for fraud detection: A case study. Proc. AAAI Workshop on AI Approaches to Fraud Detection and Risk Management (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 34–38.Google Scholar
• Levin DZ, Cross R (2004) The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Sci. 50(11):1477–1490.Link, Google Scholar
• Macskassy SA, Provost F (2007) Classification in networked data: A toolkit and a univariate case study. J. Machine Learn. Res. 8:935–983.Google Scholar
• Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using Bayesian and neural networks. Proc. First Internat. NAISO Congress on Neuro Fuzzy Technologies (ICSC-NAISO, Alberta, Canada),16–19.Google Scholar
• McGlohon M, Bay S, Anderle MG, Steier DM, Faloutsos C (2009) Snare: A link analytic system for graph labeling and risk detection. Proc. Fifteenth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 1265–1274.Crossref, Google Scholar
• Neville J, Şimşek Ö, Jensen D, Komoroske J, Palmer K, Goldberg H (2005) Using relational knowledge discovery to prevent securities fraud. Proc. Eleventh ACM SIGKDD Internat. Conf. Knowledge Discovery in Data Mining (Association for Computing Machinery, New York), 449–458.Crossref, Google Scholar
• Opsahl T, Panzarasa P (2009) Clustering in weighted networks. Soc. Networks 31(2):155–163.Crossref, Google Scholar
• Page L (2001) Method for node ranking in a linked database. US Patent 6,285,999; filed January 9, 1998; issued September 4, 2001.Google Scholar
• Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, Stanford University, Stanford, CA.Google Scholar
• Pandit S, Chau DH, Wang S, Faloutsos C (2007) NetProbe: A fast and scalable system for fraud detection in online auction networks. Proc. Sixteenth Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 201–210.Crossref, Google Scholar
• Park J, Barabási A-L (2007) Distribution of node characteristics in complex networks. Proc. Natl. Acad. Sci. USA 104(46):17916–17920.Crossref, Google Scholar
• Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explorations Newsletter 6(1):50–59.Crossref, Google Scholar
• Prakash BA, Tong H, Valler N, Faloutsos M, Faloutsos C (2010) Virus propagation on time-varying networks: Theory and immunization algorithms. Machine Learning and Knowledge Discovery in Databases (Springer, Berlin), 99–114.Crossref, Google Scholar
• Provost F (2000) Machine learning from imbalanced data sets. Proc. AAAI Workshop on Imbalanced Data Sets (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 1–3.Google Scholar
• Provost F, Dalessandro B, Hook R, Zhang X, Murray A (2009) Audience selection for on-line brand advertising: Privacy-friendly social network targeting. Proc. Fifteenth ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining (Association for Computing Machinery, New York), 707–716.Crossref, Google Scholar
• Quah JTS, Sriganesh M (2008) Real-time credit card fraud detection using computational intelligence. Expert Systems Appl. 35(4):1721–1732.Crossref, Google Scholar
• Rossi R, Neville J (2012) Time-evolving relational classification and ensemble methods. Advances in Knowledge Discovery and Data Mining (Springer, Berlin), 1–13.Crossref, Google Scholar
• Sánchez D, Vila MA, Cerda L, Serrano JM (2009) Association rules applied to credit card fraud detection. Expert Systems Appl. 36(2):3630–3640.Crossref, Google Scholar
• Šubelj L, Furlan Š, Bajec M (2011) An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems Appl. 38(1):1039–1052.Crossref, Google Scholar
• Tong H, Faloutsos C, Pan JY (2006) Fast random walk with restart and its applications. Proc. Sixth Internat. Conf. Data Mining (IEEE, Piscataway, NJ), 613–622.Crossref, Google Scholar
• Tong H, Papadimitriou S, Philip SY, Faloutsos C (2008) Proximity tracking on time-evolving bipartite graphs. Proc. SIAM Internat. Conf. Data Mining (Society for Industrial and Applied Mathematics, Philadelphia), 704–715.Crossref, Google Scholar
• Van Vlasselaer V, Bravo C, Caelen O, Eliassi-Rad T, Akoglu L, Snoeck M, Baesens B (2015) APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems 75:38–48.Crossref, Google Scholar
• Verbeke W (2012) Profit-driven data mining in massive customer networks: New insights and algorithms. Master’s thesis, KU Leuven, Leuven, Belgium.Google Scholar
• Wang JC, Chiu CC (2008) Recommending trusted online auction sellers using social network analysis. Expert Systems Appl. 34(3):1666–1679.Crossref, Google Scholar
• Yanchun Z, Wei Z, Changhai Y (2011) Detection of feedback reputation fraud in Taobao using social network theory. Proc. Internat. Joint Conf. Service Sci. (IEEE, Piscataway, NJ), 188–192.Crossref, Google Scholar