ABSTRACT
Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random forest algorithm and the Ad boost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Ad boost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.
EXISITNG :
With different frauds mostly credit card frauds, often in the news for the past few years, frauds are in the top of mind for most the world’s population. Credit card dataset is highly imbalanced because there will be more legitimate transaction when compared with a fraudulent one. As advancement, banks are moving to EMV cards, which are smart cards that store their data on integrated circuits rather than on magnetic stripes, have made some on-card payments safer, but still leaving card-not-present frauds on higher rates. According to 2017 [10], the US Payments Forum report, criminals have shifted their focus on activities related to CNP transactions as the security of chip cards were increased. Fig 2, shows the number of CNP frauds cases that were registered in respective years.
PROPOSED SYSTEM:
Card transactions are always unfamiliar when compared to previous transactions made the customer. This unfamiliarity is a very difficult problem in real-world when are called concept drift problems [1]. Concept drift can be said as a variable which changes over time and in unforeseen ways. These variables cause a high imbalance in data. The main aim of our research is to overcome the problem of Concept drift to implement on real-world scenario. Table 1, [1] shows basic features that are captured when any transaction is made.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
