Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

In today's economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments conv...

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Bibliographic Details
Published in2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 680 - 683
Main Authors Khatri, Samidha, Arora, Aishwarya, Agrawal, Arun Prakash
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2020
Subjects
Credit Card
Credit Card Fraud
Credit cards
Data models
Machine learning
Machine learning algorithms
Mathematical model
Predictive models
Sensitivity
Supervised Learning
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Summary:In today's economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some Machine Learning Algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.
DOI:10.1109/Confluence47617.2020.9057851