%0 Journal Article %1 conf/csps/XieXLGYFL18 %A Rengarajan, Nikitha Pradeep | Dr. A %D 2021 %J International Journal of Trend in Scientific Research and Development %K Credit Forest Fraud Isolation Local Machine Support Vector anomaly card detection factor outlier %N 4 %P 426-429 %T Credit Card Fraud Detection %U https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep %V 5 %X Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep

@article{conf/csps/XieXLGYFL18, abstract = {Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep }, added-at = {2021-07-14T09:00:55.000+0200}, author = {Rengarajan, Nikitha Pradeep | Dr. A}, biburl = {https://www.bibsonomy.org/bibtex/25c0e64ca180c910114be778cbb034a5d/ijtsrd}, ee = {https://doi.org/10.1007/978-981-13-6508-9_122}, interhash = {07e72ef285487fc885382f1399afd797}, intrahash = {5c0e64ca180c910114be778cbb034a5d}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Credit Forest Fraud Isolation Local Machine Support Vector anomaly card detection factor outlier}, language = {english}, month = jun, number = 4, pages = {426-429}, timestamp = {2021-07-14T09:00:55.000+0200}, title = {Credit Card Fraud Detection }, url = {https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep}, volume = 5, year = 2021 }