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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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Abstract
For clients to avoid being charged for items they did not buy, credit card companies must be able to identify fraudulent credit card transactions. To overcome such challenges, Data Science and Machine Learning might be applied. This study uses Credit Card Fraud Detection to show how machine learning can be used to model a data collection. The Credit Card Fraud Detection Issue includes modelling previous credit card transactions using information from transactions that turned out to be fraudulent.
The model is then applied to assess the likelihood of fraud in a new transaction. Our objective is to eliminate erroneous fraud classifications while detecting all fraudulent transactions. Credit card fraud detection is an excellent illustration of classification. During this process, we focused on analysing and pre-processing large data sets as well as implementing multiple anomaly detection methods.
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