Home   >   CSC-OpenAccess Library   >    Manuscript Information
Online Transaction Fraud Detection using Hidden Markov Model & Behavior Analysis
Niki Patel, Yanyan Li, Ahmad Reza Hadaegh
Pages - 59 - 72     |    Revised - 30-04-2021     |    Published - 01-06-2021
Volume - 15   Issue - 3    |    Publication Date - June 2021  Table of Contents
Fraud Detection, Online Transactions, Hidden Markov Model, Behavior Analysis.
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
1 refSeek 
2 BibSonomy 
3 J-Gate 
4 Scribd 
5 SlideShare 
A. Dal Pozzolo, O. Caelen, Yann-A¨el Le Borgne, Serge Waterschoot, and Gianluca Bontempi. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl., 41:4915–4928, 2014.
A. Roy and J. Sun and R. Mahoney and L. Alonzi and S. Adams and P. Beling, "Deep learning detecting fraud in credit card transactions," in Systems and Information Engineering Design Symposium (SIEDS), pp. 129-134, 2018.
A.Prakash (December 2012)A Novel Hidden Markov Model for Credit Card Fraud Detection. International Journal of Computer Applications, Vol.59, No.3.
About US English. (2020, November 09). Retrieved March, 2021, from https://nilsonreport.com/publication_chart_and_graphs_archive.php?1=1&year=2020.
Anderson, Keith & Durbin, Erik & Salinger, Michael. (2008). Identity Theft. Journal of Economic Perspectives. 22. 171-192. 10.1257/jep.22.2.171.
Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, and Gianluca Bontempi. Credit card fraud detection and concept-drift adaptation with delayed supervised information. 2015 International Joint Conference on Neural Networks (IJCNN).
Bunke, H., & Caelli, T. (2001). Hidden Markov models: Applications in computer vision. Singapore: World Scientific.
Daniel Jurafsky & James H. Martin. Speech and Language Processing. December 30, 2020.
Fraud detection: How machine learning systems help Reveal scams in Fintech, healthcare, and ecommerce. (2020, February 27).
H.Zhou, G.Sun . (2019). A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics. Tech Science Press, Vol.60, No.1.
John O. Awoyemi, Adebayo Olusola Adetunmbi, and Samuel Adebayo Oluwadare. Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI), pages 1–9, 2017.
K. Bennett. Legacy systems: coping with success. Published in: IEEE Software ( Volume: 12, Issue: 1, Jan. 1995). Pages 19-23. DOI: 10.1109/52.363157.
Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim and Asoke K. Nandi, "Credit card fraud detection using AdaBoost and majority voting," IEEE Access, vol. 6, pp. 14277-14284, 2018.
Nilson Company. Connected Commerce. Connectivity is Enabling Lifestyle Evolution. November 2018.
Nilson Company. Connected Commerce. Issue 1187. Dec 2020 Card Fraud Worldwide.
Rabiner, L. R. (1989, February). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. February 1989. Proceedings of the IEEE, Vol.77, No.2.
S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang and C. Jiang, "Random forest for credit card fraud detection," 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 2018, pp. 1-6, doi: 10.1109/ICNSC.2018.8361343.
Stamp, M. A Revealing Introduction to Hidden Markov Models.
Stojanovic A., Aouada D., Ottersten B Bahnsen A.C., "Cost-sensitive credit card fraud detection using Bayes minimum risk," in 12th International Conference on Machine Learning and Applications (ICMLA), pp. 333-338, 2013.
T.Chetcuti & A. Dingli,(2008) Using Hidden Markov Models in Credit Card Transaction Fraud Detection.
V.Bhusari & S.Patil. “Application of hidden Markov Model in Credit Card Fraud Detection” November 2011. International Journal of Distributed and Parallel Systems (IJDPS) Vil.2, No.6.
William N. Robinson, Andrea Aria, Sequential fraud detection for prepaid cards using hidden Markov model divergence, Expert Systems with Applications, Volume 91,2018, Pages 235-251, ISSN 0957-4174.
You Dai, Jin Yan, Xiaoxin Tang, Han Zhao and Minyi Guo, "Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies", IEEE TrustCom/BigDataSE/ISPA, pp 1644 -1651, 2016.
Miss Niki Patel
Computer Science and Information System, California State University San Marcos, San Marcos, 92096 - United States of America
Dr. Yanyan Li
Computer Science and Information System, California State University San Marcos, San Marcos, 92096 - United States of America
Dr. Ahmad Reza Hadaegh
Computer Science and Information System, California State University San Marcos, San Marcos, 92096 - United States of America