Financial fraud detection using naive bayes algorithm in highly imbalance data set. (4th July 2021)

• Record Type: Journal Article
• Authors: Gupta, Amit; Lohani, M. C.; Manchanda, Mahesh
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 24:Number 5(2021)
• Page Start: 1559
• Page End: 1572
• Publication Date: 2021-07-04
• Subjects: 68-XX -- Bigdata analytics and machine learning; Machine learning -- Credit card fraud -- Naive bayes -- Logistic regression -- Random forest -- SVM; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2021.1969733
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: This is the era, where the plastic money concept is widely adapted all over the world, but every new technology has its own loopholes also. In this scenario many types of anomalies can happen which can harm the user economically. These anomalies can be defined as frauds in financial sector. To detect these types of frauds, many techniques and models are proposed by the researchers. In this study the proposed work tries to implement an automated model using different machine learning techniques for the detection of these kinds of frauds, especially related to credit cards transactions. The proposed model applied four algorithms used in machine learning, namely Naive Bayes, Random Forest, Logistic Regression and SVM on a very large dataset to predict the fraud. Naive Bayes algorithm performance is outstanding for detection of credit card fraud among all the ML algorithms with the accuracy 80.4% and the area under the curve is 96.3%