Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
PAMPANA GNANA VENKATA SAl
, DR.P. SRINIVASULU , TULASI RAJU NETHALA
Fraud detection, deep learning, machine learning, online fraud, credit card frauds, transaction data analysis.
People could use credit cards to buy things online because they are handy and easy to use. As more people use credit cards, more people abuse them too. People who use stolen credit cards lose a lot of money, and so do banks and other financial institutions. The main goal of this research study is to find these frauds, such as those with a lot of false alarms, data that is open to the public, data that shows a big difference in class, and changes in the type of scam. There are several credit card recognition methods based on machine learning that have been written about. Think about the Extreme Learning Method, SVM, Random Forest, Decision Tree, XG Boost, and Logistic Regression. State-of-the-art deep learning algorithms are as yet expected to limit fake consumptions in light of their poor accuracy. Utilizing the latest deep learning strategies has been the objective. Machine learning and deep learning theories were differentiated to come by great results. The whole scientific stealing research uses the European Card Benchmark sample. First, the information was put through a machine learning method, which helped find frauds to some degree. In the end, three designs based on convolutional neural networks are used to make scam detection work better. By adding more levels, the accuracy of recognition went up by a large amount. A full observational study was carried out using the most up-to-date models and changing the number of secret layers and epochs. By looking at the study work, we can see that the results got better. The accuracy went up to 99.9%, the f1-score went up to 85.71%, the precision went up to 98%, and the AUC curves had ideal values of 93%, 98%, 85.71%, and 99.9%. The suggested model does a better job of recognizing credit cards than modern machine learning and deep learning methods. We also tried using deep learning and adjusting the data to get the false negative rate down. There are good ways to spot credit card theft in the real world that are being shown.
"Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.b310-b318, October-2024, Available :https://ijnrd.org/papers/IJNRD2410137.pdf
Volume 9
Issue 10,
October-2024
Pages : b310-b318
Paper Reg. ID: IJNRD_301238
Published Paper Id: IJNRD2410137
Downloads: 00079
Research Area: Science and Technology
Country: West Godavari , Andhra Pradesh , India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
