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Machine Learning Techniques Used for Detection Fraudulent Transactions
Subject area: Science,Engineering and Technology · Area of research: Engineering
Abstract
Fraud is a big problem for banks, business, and people. It can cause many harm like losing money, trust, and respect. The old ways of detecting fraud are not so good because they are slow and can miss tricky types of fraud. But machine learning can help by catching fraud right away. This paper talks about how to use machine learning to find fraud. There are different ways to use machine learning like supervised learning and unsupervised learning, decision trees, neural networks, and finding anomalies. But there are also some problems withusing machine learning for fraud, like making sure the data is good, keeping privacy safe, and doing the right thing. In this paper there are examples of how people use machine learning to stop fraud in credit cards, insurance, and medical care. These examples show that machine learning can be very good at catching fraud right away.
Keywords
Fraud detection, machine learning, Supervised learning, unsupervised learning, decision tree, neural networks, anomaly detection, data privacy ethical considerations.
References
[1] Randhawa, Kuldeep, et al. “Credit Card Fraud Detection Using AdaBoost and Majority Voting.” IEEE Access, vol. 6, 2018, pp. 14277–14284., doi:10.1109/access.2018.2806
[2] A. Mishra, C. Ghorpade, “Credit Card Fraud Detection on the Skewed Data Using Various Classification and Ensemble Techniques” 2018 IEEE International Students' Conference on Electrical, Electronics.
[3] Z. Kazemi, H. Zarrabi, “Using deep networks for fraud detection in the credit card transactions”, Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on pp. 630-633. IEEE.
[4] Chen, X., & Ren, Z. (2020). Application of machine learning algorithms in fraud detection of online payment systems. Journal of Electronic Commerce Research, 21(3), 262-278.
[5] Jiang, Changjun et al. “Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback
[6] Mechanism.” IEEE Internet of Things Journal 5 (2018): 3637-3647.
[7] Randhawa, Kuldeep, et al. “Credit Card Fraud Detection Using AdaBoost and Majority Voting.” IEEE Access, vol. 6, 2018, pp. 14277–14284., doi:10.1109/access.2018.2806420
How to cite this paper
@article{1704427,
author = {Ankita Rai},
title = {Machine Learning Techniques Used for Detection Fraudulent Transactions},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {11},
pages = {264-267},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704427.pdf},
abstract = {Fraud is a big problem for banks, business, and people. It can cause many harm like losing money, trust, and respect. The old ways of detecting fraud are not so good because they are slow and can miss tricky types of fraud. But machine learning can help by catching fraud right away. This paper talks about how to use machine learning to find fraud. There are different ways to use machine learning like supervised learning and unsupervised learning, decision trees, neural networks, and finding anomalies. But there are also some problems withusing machine learning for fraud, like making sure the data is good, keeping privacy safe, and doing the right thing. In this paper there are examples of how people use machine learning to stop fraud in credit cards, insurance, and medical care. These examples show that machine learning can be very good at catching fraud right away.},
keywords = {Fraud detection, machine learning, Supervised learning, unsupervised learning, decision tree, neural networks, anomaly detection, data privacy ethical considerations.},
month = {May},
}