Abstract
Abstract Financial fraud is a problem that has proved to be a menace and has a huge impact on the financial industry. Data mining is one of the techniques which has played an important role in credit card fraud detection in transactions which are online. Credit card fraud detection has proved to be a challenge mainly due to the 2 problems that it poses - both the profiles of fraudulent and normal behaviours change and data sets used are highly skewed. The performance of fraud detection is affected by the variables used and the technique used to detect fraud. This paper compares the performance of logistic regression, K-nearest neighbors, random forest, naive bayes, multilayer perceptron, ada boost, quadrant discriminative analysis, pipelining and ensemble learning on the credit card fraud data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
