Credit Card Fraud Detection Using Random Forest and Local Outlier Factor
Abhilasha Kulkarni · International Journal for Research in Applied Science and Engineering Technology · 2019
With the evolution of new technology, the use of credit cards has augmented. As credit card becomes the trendiest style of payment for online payment as well as manual payment, the numbers of credit card frauds are increasing day by day. It is necessary to curb the credit card frauds as it causes huge amount of financial loss. Many modern techniques based on Artificial Intelligence, Data mining, Machine Learning, Sequence Alignment, Genetic programming, etc are available that can be used in detecting the fraudulent transactions. We have used machine learning based algorithms. Machine Learning is a subset of Artificial Intelligence. It is a scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using any explicit instructions, relying on patterns and inference instead. Machine learning is the technology in which we train the machine by using various algorithms and make the machine capable enough to take its own decisions. Machine Learning consists of many algorithms that can be used in fraud detection such as Random Forest, Local Outlier Fraction, Isolation Forest, Naïve Bayes, K-nearest Neighbour, Hidden Markov Model, Neural Networks, etc that can be used in fraud detection. In this paper we have done comparative study of Random Forest algorithm and Local Outlier Factor.