We analyzed a dataset of fraudulent credit card transactions to uncover patterns in fraudulent transactions and to demonstrate the importance of focusing on suspicious transactions. We argue that revealed patterns in fraudulent transactions may help financial institutions update their practices and develop innovative mechanisms and systems to improve their performance at preventing and detecting credit card frauds.
AleskerovE.FreislebenB.RaoB. (1997). CARDWATCH: A neural network based database mining system for credit card fraud detection. In Computational Intelligence for Financial Engineering. Proceedings of the IEEE/IAFE (pp. 220–226). Piscataway, NJ: IEEE.
2.
BeckerR.A.VolinskyC.WilksA.R. (2010). Fraud detection in Telecommunications: History and lessons learned. Technometrics, 52(1), 20–33.
3.
BhattacharyyaS.JhaS.TharakunnelK.WestlandC. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602–613.
4.
BoltonR.J.HandD.J. (2001). Unsupervised profiling methods for fraud detection. Proceedings from, Conference on Credit Scoring and Credit Control, 7, Edinburgh, UK, September, 5–7.
BoseR. (2006). Intelligent Technologies for Managing Fraud and Identity Theft, In Proceedings of the Third International Conference on Information Technology: New Generations(ITNG’06).
7.
BreimanL.FriedmanJ.H.OlshenR.A.StoneC.J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.
8.
ChenR.ChiuM.HuangY.ChenL. (2004). Proceedings from IDEAL 2004: Detecting Credit Card Fraud by Using Questionnaire Responded Transaction Model Based on Support Vector Machines.
9.
ChenR.C.ChenT.S.LinC. (2006). A new binary support vector system for increasing detection rate of credit card fraud. International Journal of Pattern Recognition, 20(2), 227–239.
10.
ClarkP.NiblettT. (1989). The CN2 induction algorithm. Machine Learning, 3(4), 261–285.
11.
CohenW. (1995). Fast effective rule induction. In Proceedings of the 12th International Conference on machine Learning (pp. 115–123). Palo Alto, CA: Morgan Kauffman.
12.
CortesC.PregibonD. (1998). Giga-mining. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining(pp. 174–178). Melno Park, CA: AAAI Press.
13.
CyberSource. (2009). Online fraud report: Online payment, fraud trends, merchant practices, and bench marks. Retrieved from http://forms.cybersource.com/forms/FraudReport2009NACYBSwww020309.
14.
DorronsoroJ.R.GinelF.SanchezC.Santa CruzC. (1997). Neural fraud detection in credit card operations. IEEE Transactions on Neural Networks, 8, 827–834.
15.
Federal Reserve. (2007). The 2007 federal reserve payments study. Retrieved from http://www.frbservices.org/files/communications/pdf/research/2007_payments_study.pdf.
16.
ForrestS.HofmeyrS.SomayajiA.LongstaffT. (1996). A sense of self for unix processes. Proceedings of the 1996 IEEE Symposium on Security and Privacy. Los Alamitos, CA.
17.
GhoshS.ReillyD.L. (1994). Credit card fraud detection with a neural-network. In NunamakerJ. F.SpragueR. H. (Eds.) Proceedings of the 27th annual Hawaii international conference on system science, Vol 3: Information systems: DSS/knowledge-based systems (pp. 621–630). Los Alamitos, CA, USA.
18.
HandD.J. (1981). Discrimination and classification. Chichester: Wiley.
19.
HandD.J. (1997). Construction and assessment of classification rules. Chichester: Wiley.
20.
HandD.J.HenleyW.E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of Royal Statistics Society. Series A, 160(3), 523–541.
21.
HandD.J.BluntG. (2001). Prospecting for gems in credit card data. IMA Journal of Management Mathematics, 12, 173–200.
22.
HandD.J.BluntG. (2001a). Unsupervised profiling methods for fraud detection. Proceedings from, Conference of Credit Scoring and Credit Control, 7, Edinburgh, UK, September5–7.
23.
HandD.J.WhitrowC.AdamsN.M.JuszczakP.WestonD. (2008). Performance criteria for plastic card fraud detection. Journal of the Operational Research Society, 59(7), 956–962.
24.
HillT.P. (1995). A statistical derivation of the significant-digit law. Statistical Science, 10(4), 354–363.
25.
HungE.CheungD.W. (1999). Parallel Algorithm for Mining Outliers in Large Database. Proceedings 9th International Database Conference (IDC’99). Hong Kong, July15–17.
26.
HungE.CheungD.W. (2002). Parallel mining of outliers in large database. Distributed and Parallel Databases, 12(1), 5–26.
27.
JhaS.GuillenM.WestlandC.J. (2012). Employing transaction aggregation strategy to detect credit card fraud. Expert Systems with Applications, 39(16), 12650–12657.
28.
KosoresowA.P.HofmeyrS.A. (1997). Intrusion detection via system call traces. IEEE Software, 14(5), 24–42.
29.
KouY.Chang-TienL.SirwongwattanaS.HuangY.P. (2004). Survey of fraud detection techniques. In IEEE International Conference on Networking, Sensing and Control, 749–754.
30.
KrivkoM. (2010). A Hybrid Model for Plastic Card Fraud Detection Systems. Expert Systems with Applications, 37(8), 6070–6076.
31.
LaneT.BroadleyC.E. (1998). Temporal sequence learning and data reduction for anomaly detection. Proceedings of the 5th ACM Conference on Computer and Communications Security (CCS-98), New York, 150–158.
32.
LevittS.D.DubnerS.J. (2009). Super freakonomics. New York: HarperCollins.
33.
MaesS.TuylsK.VanschoenwinkelB.ManderickB. (2002). Credit card fraud detection using bayesian and neural networks. Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies, Havana, Cuba.
34.
McLachlanG.J. (1992). Discriminant analysis and statistical pattern recognition. New York: Wiley.
35.
NigriniM.J. (1999). I’ve got your number. Journal of Accountancy, 187(5), 79–83.
36.
NigriniM.J.MittermaierL.J. (1997). The use of Benford’s law as an aid in analytical procedures. Auditing; A Journal of Practice and Theory, 16(2), 52–67.
37.
PhuaC.LeeV.SmithK.GaylerR. (2005). A comprehensive survey of data mining-based fraud. Detection Research, Clayton School of information Technology, Monash University.
38.
ProvostF. (2002). Comment on Bolton and Hand. Statistical Science, 17(3), 249–251.
39.
QuD.VetterB.M.WangF.NarayanR.WuS.F.HouY.F.GongF.SargorC. (1998). Statistical anomaly detection for link-state routing protocols. Proceedings Sixth International Conference on Network Protocols. 62–70, IEEE.
QuinlanJ.R. (1993). C4.5: Programs for machine Learning. San Mateo, CA: Morgan Kauffman.
42.
RajS.B.E.PortiaA.A. (2011). Analysis on credit card fraud detection methods. International Conference on Computer, Communication and Electrical Technology. March18 & 19.
43.
RipleyB.D. (1996). Pattern recognition and neural networks. Cambridge: Cambridge University Press.
44.
ShenA.RenchngT.DengY. (2007). Application of classification models on credit card fraud detection. Service Systems and Service Management, 2007, International Conference on Service Systems and Service Management (pp. 1–4), June, IEEE.
45.
SmythP.ElkanC. (2010). Creativity helps influence prediction precision. Communications of the ACM, 53(4), 88.
46.
SmythP.PregibonD.FaloutsosC. (2002). Data-driven evolution of data mining algorithm. Communications of the ACM, 45(8), 33–37.
47.
SrivastavaA.KunduA.SuralS.MajumdarA. (2008). Credit card fraud detection using hidden Markov model. IEEE Transactions on Dependable and Secure Computing, 5(1), 37–48.
WhitrowC.HandD.J.JuszczakP.WestonD.AdamsN.M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
