Credit Card Fraud Detection Using Hidden Markov Model

By Abhinav Srivastava, Amlan Kundu, Shamik Sural, Arun Kumar Majumdar. IEEE Transactions on Dependable and Secure Computing, 2012

558 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/381e3ed88ee5d2812d2c8c314e58bc160e62bbf6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhenchuan Li
2. Liyun He-Guelton
3. Changjun Jiang
4. Asoke Kumar Nandi
5. Sylvie Calabretto
6. Charu C. Aggarwal
7. Chee Peng Lim
8. Manjeevan Seera

In Computer Vision and Pattern Recognition

1. Asoke Kumar Nandi
2. Charu C. Aggarwal
3. Chee Peng Lim
4. Manjeevan Seera
5. Longbing Cao
6. Michael Granitzer
7. Olivier Caelen
8. Leo Liberti

In Media Technology

1. Asoke Kumar Nandi
2. Charu C. Aggarwal
3. Longbing Cao

Citations in the corpus

• Outlier Analysis (2013, methodology): “Recently, discrete sequence methods of Chapter 9 based on Hidden Markov Models have also been used [422].”
• Random forest for credit card fraud detection (2018, methodology): “[5] model the sequence of transaction features in credit card transaction processing using a hidden markov model (HMM) and demonstrate its effectiveness on the detection of frauds.”
• Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis (2010, background): “Chandra, Shalini; Srivastava, Shirish C.; and Theng, Yin-Leng (2010) "Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis," Communications of the Association for Information Systems: Vol.”
• Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, methodology): “There are also some unsupervised methods, such HMM [56] and cluster [46], targeting unlabeled data sets.”
• Sequence classification for credit-card fraud detection (2018, methodology): “The likelihood is then used as a score to effectively perform anomaly detection [Srivastava et al., 2008].”
• Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, background): “With the rise of e-commerce in the past decade, the use of credit cards has increased dramatically [3].”
• Data mining for credit card fraud: A comparative study (2011, unknown): “Other techniques reported for credit card fraud detection include case based reasoning [48] and more recently, hidden Markov models [45].”
• Deep learning detecting fraud in credit card transactions (2018, methodology): “used HMM in credit card fraud detection in eCommerce transactions and found that HMM detected fraud correctly 80% of the cases [20].”