This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Meenu , Swati Gupta , Sanjay Patel, Surender Kumar, Goldi Chauhan
Anomaly Detection is a method of identifying the suspicious occurrence of events and data items that could create problems for the concerned authorities. Data anomalies are usually associated with issues such as security issues, server crashes, bank fraud, building structural flaws, clinical defects, and many more. Credit card fraud has now become a massive and significant problem in today's climate of digital money. These transactions carried out with such elegance as to be similar to the legitimate one. So, this research paper aims to develop an automatic, highly efficient classifier for fraud detection that can identify fraudulent transactions on credit cards. Researchers have suggested many fraud detection methods and models, the use of different algorithms to identify fraud patterns. In this study, we review the Isolation forest, which is a machine learning technique to train the system with the help of H2O.ai. The Isolation Forest was not so much used and explored in the area of anomaly detection. The overall performance of the version evaluated primarily based on widely-accepted metrics: precision and recall. The test data used in our research come from Kaggle.
Assistant Professor, Department of Computer Science, Amity University, Gurugram, Haryana, India (email:email@example.com)
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