Credit Card Fraud Detection Using Machine Learning Approach

KANAL BHADRESH SONI

Abstract


Using new spam technologies to carry out internet banking fraud refers to shifting and withdrawing money from the user’s balance account without it’s authorization. Credit card fraud pops into the mind so far in the current scenario when the concept of fraud bursts into some conversation. Credit card fraud has escalated tremendously in recent times due to the incredible growth in credit card purchases. In order to assess, identify or prevent undesirable conduct, fraud detection requires tracking the purchase behavior of users/customers. The purpose of this project is to predict the genuine and fraud transactions with respect to the amount of the transaction utilizing various machine learning approaches like Logistic Regression, Decision Trees, Support Vector Machine, Naïve Bayes, Random Forest and K-Nearest Neighbor. The model built who has greater accuracy and precision is considered to be best fit for this system.


Keywords


Application of Machine Learning; Decision Trees; K-Nearest Neighbor; Logistic Regression; Naïve Bayes; Random Forest; Support Vector Machine

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References


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DOI: https://doi.org/10.15408/aism.v4i2.20570 Abstract - 0 PDF - 0

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Department of Information Systems, Faculty of Science and Technology,
Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
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Applied Information System and Management (AISM) by Department of Information Systems, Faculty of Science and Technology, Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Indonesia is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://journal.uinjkt.ac.id/index.php/aism.