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@article{155230,
        author = {A.S.Malini and J.M Shajitha Banu and M.I Sharmila Fathima},
        title = {Credit Card Fraud Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {200-205},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155230},
        abstract = {Anomaly Detection is a way of recognizing suspicious occurrences of events and data items that may cause difficulties for the authorities. Security difficulties, server breakdowns, bank fraud, building structural weaknesses, clinical abnormalities, and other issues are often related with data anomalies. In today's digital money milieu, credit card fraud has become a big and major concern. These transactions are carried out with such finesse that they resemble legal transactions. As a result, the goal of this research work is to create an autonomous, highly efficient classifier for fraud detection that can detect fraudulent credit card transactions. Many fraud detection strategies and models have been proposed by researchers, including the use of various algorithms to identify fraud trends. In this paper, we look at the Isolation forest, which is a machine learning approach used to train the system using H2O.ai. In the domain of anomaly detection, the Isolation Forest was not widely utilized or researched. The version's overall performance was tested largely using commonly established metrics: accuracy and recall. Kaggle provided the test data for our study.},
        keywords = {Anomaly Detection, Isolation Forest, Credit Card Fraud Detection, Classification using Machine Learning.},
        month = {},
        }