research-article

Machine Learning for Credit Card Fraud Detection

Authors:

Yuxin Gao,

Shuoming Zhang,

Jiapeng LuAuthors Info & Claims

ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics

Pages 213 - 219

https://doi.org/10.1145/3473714.3473749

Published: 13 August 2021 Publication History

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Abstract

With the development of E-bank, the use of credit cards gets an unprecedented improvement as well as the problem of credit card fraud. To overcome this problem, we need automatic systems to finish the fraud detection. The number of monitored account data is so large that our human resources are unable to detect the whole dataset. Also, since the number of fraudulent transactions is (fortunately) much smaller than the legitimate ones, the data distribution is unbalanced, skewed towards non-fraudulent observations. To solve the problem of unbalanced dataset, there exist many learning algorithms which are used to underperform this kind of problem; to improve the accuracy or predicting, there exist many methods like over or under sampling.

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Reid A. Johnson Andrea Dal Pozzolo Olivier Caelen and Gianluca Bontempi. "Calibrating Probability with Undersampling for Unbalanced Classification." In: Symposium on Computational Intelligence and Data Mining (CIDM) (2015).

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Andrea. Dal Pozzolo. "Learned lessons in credit card fraud detection from a practitioner perspective". In: (2014).

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Fabrizio Carcillo. "Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization." In: International Journal of Data Science and Analytics (2019).

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Andrea. Dal Pozzolo. "Credit card fraud detection: a realistic modeling and a novel learning strategy". In: IEEE Transactions on Neural Networks and Learning Systems (2017).

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Andrea Dal Pozzolo. "Adaptive Machine learning for credit card fraud detection". In: (2015).

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Cited By

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Singh IAtri PGoyal PBhatnagar PShekhar R(2023)Enhancing Credit Card Fraud Detection with hybrid Squirrel Search - Honey Badger Algorithm Optimized Neural Network2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306817(1-7)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10306817
Correia MRodrigues L(2023)Security and PrivacyMultidisciplinary Perspectives on Artificial Intelligence and the Law10.1007/978-3-031-41264-6_5(81-101)Online publication date: 27-Dec-2023
https://doi.org/10.1007/978-3-031-41264-6_5

Index Terms

Machine Learning for Credit Card Fraud Detection
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Human and societal aspects of security and privacy
  2. Network security

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Published In

ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics

June 2021

807 pages

ISBN:9781450390231

DOI:10.1145/3473714

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Chongqing Univ.: Chongqing University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2021

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ICCIR 2021

ICCIR 2021: 2021 International Conference on Control and Intelligent Robotics

June 18 - 20, 2021

Guangzhou, China

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ICCIR '21 Paper Acceptance Rate 131 of 239 submissions, 55%;

Overall Acceptance Rate 131 of 239 submissions, 55%

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Cited By

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Singh IAtri PGoyal PBhatnagar PShekhar R(2023)Enhancing Credit Card Fraud Detection with hybrid Squirrel Search - Honey Badger Algorithm Optimized Neural Network2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306817(1-7)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10306817
Correia MRodrigues L(2023)Security and PrivacyMultidisciplinary Perspectives on Artificial Intelligence and the Law10.1007/978-3-031-41264-6_5(81-101)Online publication date: 27-Dec-2023
https://doi.org/10.1007/978-3-031-41264-6_5

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Fraud detection in credit card transaction using neural networks

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A comparison of machine learning algorithms for credit card fraud detection