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Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression.

Jiwon Chung1, Kyungho Lee1

  • 1School of Cybersecurity, Korea University, Seoul 02841, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces an improved algorithm for credit card fraud detection, combining three machine learning models with conditional logic. The enhanced method significantly boosts fraud identification accuracy, outperforming single-model approaches.

Keywords:
KNNLDAcredit card fraud detectionlinear regressionrecall analysissensitivity analysistrue positive rate analysis

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • The rise of e-commerce and Internet of Things (IoT) devices has increased the global concern of credit card fraud.
  • Accurate and efficient identification of fraudulent transactions is crucial for financial security.

Purpose of the Study:

  • To propose an improved algorithm for highly sensitive credit card fraud detection.
  • To enhance the accuracy and recall of fraud identification systems.

Main Methods:

  • Leveraged three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression.
  • Applied conditional statements (IF/THEN) and operators (>, <) to refine model outputs.
  • Extracted features using the proposed hybrid strategy.

Main Results:

  • Achieved high recall rates of 1.0000, 0.9701, 1.0000, and 0.9362 across four distinct fraud datasets.
  • Demonstrated superior performance compared to single machine learning models in terms of recall.

Conclusions:

  • The proposed methodology offers a more effective approach to credit card fraud detection.
  • The integration of multiple machine learning models with conditional logic enhances detection sensitivity and accuracy.