A Survey on Credit Card Fraud Detection Using Machine Learning

Nowadays digitalization gaining popularity because of seamless, easy and convenience use of e-commerce. It became very rampant and easy mode of payment. People choose online payment and e-shopping; because of time convenience, transport convenience, etc. As the result of huge amount of e-commerce use, there is a vast increment in credit card fraud also. Fraudsters try to misuse the card and transparency of online payments. Thus to overcome with the fraudsters activity become very essential. The main aim is to secure credit card transactions; so people can use e-banking safely and easily. To detecting the credit card fraud there are various techniques which are based on Deep learning, Logistic Regression, Naive Bayesian, Support Vector Machine (SVM), Neural Network, Artificial Immune System, K Nearest Neighbor, Data Mining, Decision Tree, Fuzzy logic based System, Genetic Algorithm etc.

[1]  Arti Mohanpurkar,et al.  Credit card fraud detection using Hidden Markov Model , 2011, 2011 World Congress on Information and Communication Technologies.

[2]  Dominik Olszewski,et al.  Fraud detection using self-organizing map visualizing the user profiles , 2014, Knowl. Based Syst..

[3]  Ekrem Duman,et al.  Detecting credit card fraud by Modified Fisher Discriminant Analysis , 2015, Expert Syst. Appl..

[4]  Weili. Ong,et al.  Real time credit card fraud detection using computational intelligence , 2011 .

[5]  Masoumeh Zareapoor,et al.  Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria , 2012 .

[6]  Ekrem Duman,et al.  Detecting credit card fraud by decision trees and support vector machines , 2011 .

[7]  Wolfgang Banzhaf,et al.  Combatting financial fraud: a coevolutionary anomaly detection approach , 2008, GECCO '08.

[8]  Maumita Bhattacharya,et al.  Intelligent Financial Fraud Detection: A Comprehensive Review , 2015 .

[9]  Gianluca Bontempi,et al.  Learned lessons in credit card fraud detection from a practitioner perspective , 2014, Expert Syst. Appl..

[10]  Chao-Hsien Chu,et al.  A Review of Data Mining-Based Financial Fraud Detection Research , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  Ekrem Duman,et al.  Detecting credit card fraud by genetic algorithm and scatter search , 2011, Expert Syst. Appl..

[12]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[13]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[14]  Pourya Shamsolmoali,et al.  Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier , 2015 .

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

[16]  Koen Vanhoof,et al.  A business process mining application for internal transaction fraud mitigation , 2011, Expert Syst. Appl..

[17]  Graham J. Williams,et al.  On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms , 2000, KDD '00.

[18]  Djamila Aouada,et al.  Feature engineering strategies for credit card fraud detection , 2016, Expert Syst. Appl..

[19]  Monique Snoeck,et al.  APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions , 2015, Decis. Support Syst..

[20]  Mohammad Kazem Akbari,et al.  A novel model for credit card fraud detection using Artificial Immune Systems , 2014, Appl. Soft Comput..

[21]  Peter Beling,et al.  Horse race analysis in credit card fraud—deep learning, logistic regression, and Gradient Boosted Tree , 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).

[22]  Vadlamani Ravi,et al.  Detection of financial statement fraud and feature selection using data mining techniques , 2011, Decis. Support Syst..

[23]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..