%0 Journal Article %1 button2013power %A Bhiaya, Tripti Gautam | Ghanshyam Sahu | Lalit Kumar P. %D 2023 %J INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT %K Card, Credit Detection Detection, Framework, Fraud Supervised Techniques Unsupervised and %N 7 %P 6 %T Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm %U https://www.ijtsrd.com/computer-science/other/60102/credit-card-fraud-detection-using-hybrid-machine-learning-algorithm/tripti-gautam %X As we know and living in the era of digital world, Credit card fraud is increasing rapidly by transactions of unauthorized or any fraudulent use of someone else information of credit card to purchase and obtain benefits of financial. The victims of credit card fraud may have severe repercussions. Financial losses, harm to credit scores, and the trouble of dealing with unauthorized transactions can all arise from it. Secure your card information, keep a close eye on your account activity, and alert your card issuer right away to any odd transactions if you want to prevent credit card theft. To help combat fraud, many financial institutions additionally provide extra security features like two factor authentication and fraud detection systems. To resolve these problem we developed a system of Credit Card Fraud detection by hybrid techniques of machine learning which combines supervised and unsupervised methods to improve the system of fraud detection. In this paper we are using machine learning algorithms like K Nearest Neighbor, Logistic Regression and XGBoost model and we had made a comparison of accuracy score with other different models by using the data of European Cardholders 2013, by that data we had make comparison and decided that which model is best for defining the fraud system of credit card. Tripti Gautam | Ghanshyam Sahu | Lalit Kumar P. Bhiaya "Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60102.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/60102/credit-card-fraud-detection-using-hybrid-machine-learning-algorithm/tripti-gautam

@article{button2013power, abstract = {As we know and living in the era of digital world, Credit card fraud is increasing rapidly by transactions of unauthorized or any fraudulent use of someone else information of credit card to purchase and obtain benefits of financial. The victims of credit card fraud may have severe repercussions. Financial losses, harm to credit scores, and the trouble of dealing with unauthorized transactions can all arise from it. Secure your card information, keep a close eye on your account activity, and alert your card issuer right away to any odd transactions if you want to prevent credit card theft. To help combat fraud, many financial institutions additionally provide extra security features like two factor authentication and fraud detection systems. To resolve these problem we developed a system of Credit Card Fraud detection by hybrid techniques of machine learning which combines supervised and unsupervised methods to improve the system of fraud detection. In this paper we are using machine learning algorithms like K Nearest Neighbor, Logistic Regression and XGBoost model and we had made a comparison of accuracy score with other different models by using the data of European Cardholders 2013, by that data we had make comparison and decided that which model is best for defining the fraud system of credit card. Tripti Gautam | Ghanshyam Sahu | Lalit Kumar P. Bhiaya "Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-6 , December 2023, URL: https://www.ijtsrd.com/papers/ijtsrd60102.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/60102/credit-card-fraud-detection-using-hybrid-machine-learning-algorithm/tripti-gautam }, added-at = {2024-01-23T10:31:36.000+0100}, author = {Bhiaya, Tripti Gautam | Ghanshyam Sahu | Lalit Kumar P.}, biburl = {https://www.bibsonomy.org/bibtex/23504feaed0ea2caff79c5c0524133cab/ijtsrd}, description = {Power failure: why small sample size undermines the reliability of neuroscience : Article : Nature Reviews Neuroscience}, interhash = {bf85f9b8b99021cd27ab7a4ac7adfcc2}, intrahash = {3504feaed0ea2caff79c5c0524133cab}, issn = {2456-6470}, journal = {INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT}, keywords = {Card, Credit Detection Detection, Framework, Fraud Supervised Techniques Unsupervised and}, language = {english}, month = dec, number = 7, pages = 6, timestamp = {2024-01-23T10:31:36.000+0100}, title = {Credit Card Fraud Detection Using Hybrid Machine Learning Algorithm }, url = {https://www.ijtsrd.com/computer-science/other/60102/credit-card-fraud-detection-using-hybrid-machine-learning-algorithm/tripti-gautam}, year = 2023 }