Anomaly Detection and Local Outlier Factor for Credit Card Fraud Detection
Gokul Hn, Achal Sai R, A Adithi, Amaresh B Patil, Gururaj Murtugudde · Journal of Emerging Technologies and Innovative Research · 2020
Nowadays, as internet speed has increased and the prices of the mobile have decreased very much in past few years. Also the data prices too are very much affordable to most of the people. This has resulted into the digitization of most of the institutes as it is easy and convenient for the people and also for the authority to maintain the records. So it resulted in most of the banks and other institutes receiving and transferring money through credit card. But with the hackers and other cyber criminals around credit card system is very easy to perform fraud. These credit card fraud creates financial loss for customers and companies and everyday fraudsters find new technique to commit the fraud. The possibilities of the fraud transaction are very less but it is not negligible and even having one fraud transaction is unacceptable because it is crime and we can’t neglect even if amount is less as it harms. So this project aims at analyzing various classification techniques using various metrics for judging various classifiers. This model aims at improving fraud detection rather than misclassifying a genuine transaction as fraud. After using the algorithms such as Isolation Forest and Local Outlier Factor, a detailed report is given in this paper.