Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting

Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS

Paper ID: EIJTEM_2022_9_2_8-15

Author's Name: Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS

Volume: 9

Issue: 2

Year: 2022

Page No: 8-15

Abstract:

Financial businesses face a major challenge with credit card theft, which results in billions of dollars in annual losses. Due to confidentiality concerns, there are limited research studies on the analysis of real-world credit card data. This study employs machine learning algorithms to identify credit card fraud. Standard models are used initially, followed by hybrid techniques using AdaBoost and majority voting techniques. The effectiveness of the model is evaluated using a publicly accessible credit card dataset, as well as a batch of actual credit card data from a financial institution. Additionally, noise is introduced to the data samples to assess the robustness of the algorithms. The experimental findings suggest thatthe majority voting approach performs well.

Keywords: Financial businesses, Credit Card Theft, Fraud Detection, Neural Networks, Machine Learning, Adaboost, Majority voting techniques.

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Standard APA style format.

Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS (2022). Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting. Excel International Journal of Technology, Engineering and Management, 9(2), 8-15. Retrieved from https://exceljournals.org.in/detail.php?id=618

Best for LaTeX documents.

@article{MachineLe_2022,
  author    = {{Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS}},
  title     = {{Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting}},
  journal   = {Excel International Journal of Technology, Engineering and Management},
  volume    = 9,
  number    = 2,
  pages     = {8-15},
  year      = 2022,
  url       = {https://exceljournals.org.in/detail.php?id=618},
}

Supported by EndNote, Mendeley, Zotero.

TY  - JOUR
AU  - Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS
TI  - Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting
JO  - Excel International Journal of Technology, Engineering and Management
VL  - 9
IS  - 2
SP  - 8
EP  - 15
PY  - 2022
UR  - https://exceljournals.org.in/detail.php?id=618
ER  -

Specific format for RefWorks manager.

RT Journal Article
A1 Malavika Joshi , Shada Manogna , Mrs. Beulah J Karthikeyan and Dr. Sankara Sarma KVSSRS
T1 Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting
JF Excel International Journal of Technology, Engineering and Management
VO 9
IS 2
SP 8
EP 15
YR 2022
LK https://exceljournals.org.in/detail.php?id=618

Excel International Journal of Technology, Engineering & Management

2.5

8.8

Machine Learning-Based Credit Card Fraud Detection: Evaluating Standard and Hybrid Techniques with Majority Voting