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• W4377964462 abstract "As the E-commerce sector is getting large, the use of electronic money and is getting wider and wider. Credit cards are the most useful and easy tools for payment. It is easy to use and reduces the efforts made by humans. But with advantages some disadvantages also come hand in hand. Many frauds take place while making the transactions and due to this many people lose millions of money. Hence, there need to be a detection system so that people can make the transactions without the fear of frauds. In today’s time there are many technologies which can help in making such a system. Some technologies are “Neural Network, Artificial Intelligence, Bayesian Network, Data mining, Artificial Immune System, K-nearest neighbor algorithm, Decision Tree, Fuzzy Logic Based System, Support Vector Machine, Machine learning, Genetic Programming etc”. This paper will include many surveys which will be conducted in which people will use different techniques to make a strong system. The work will also be aiming at making a strong detection system using libraries like numpy, sklearn and other py libraries. The problem is solved by using a classifier which can differentiate between fraud and legit transactions based on the class and time. The dataset contains 31 columns among which 28 columns are named as v1, v2, v3…. Due to security purposes, 2 columns are time and amount [1]. The total amount of transactions were 283.806 with only 492 fraud cases and rest legit transactions. In today’s time there are credit cards in the market for kids who are under 18 as well. Therefore it is important for a system to be developed for safety. Fraudsters can use the money for many illegal practices as well. This paper will use Random Forest Classifier and Decision tree to test the dataset [2]. The dataset is of some card holders from Europe." @default.
• W4377964462 created "2023-05-25" @default.
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• W4377964462 date "2023-01-23" @default.
• W4377964462 modified "2023-10-18" @default.
• W4377964462 title "An Ensemble Of Machine And Deep Learning Models For Real Time Credit Card Scam Recognition" @default.
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• W4377964462 doi "https://doi.org/10.1109/iccci56745.2023.10128473" @default.
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