Abstract
Now a day’s online transactions have become an important and necessary part of our lives. It is vital
that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged
for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions
are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project
intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. Fraud
detection has become an important tool and probably the best way to stop such frauds. The Credit Card Fraud
Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be
fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is
to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
Publisher
Institute for Advanced Studies
Cited by
3 articles.
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1. A Comparative Analysis of Machine Learning Algorithms for Credit Card Fraud Detection;2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC);2024-05-02
2. Securing Fintech: A Machine Learning Approach for Credit Card Fraud Detection;2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS);2024-04-17
3. Data and Model Centric Approaches for Card Fraud Detection;2023 International Conference on Computer and Applications (ICCA);2023-11-28