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Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection.

N Prabhakaran1, R Nedunchelian2

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A new oppositional cat swarm optimization-based deep learning model (OCSODL-CCFD) effectively detects credit card fraud. This advanced technique enhances e-payment security by accurately identifying fraudulent transactions.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Rising e-commerce and communication technology have led to increased credit card fraud.
  • Fraudulent transactions negatively impact financial institutions and customers, necessitating robust detection methods.
  • Existing machine learning and deep learning models offer potential for effective credit card fraud detection (CCFD).

Purpose of the Study:

  • To introduce a novel oppositional cat swarm optimization-based deep learning model (OCSODL-CCFD) for credit card fraud detection.
  • To enhance the accuracy and efficiency of identifying and classifying fraudulent credit card transactions.
  • To improve the trustworthiness of electronic payment systems.

Main Methods:

  • Developed a new oppositional cat swarm optimization (OCSO) algorithm for optimal feature selection.
  • Employed the chaotic krill herd algorithm (CKHA) for hyperparameter tuning of the bidirectional gated recurrent unit (BiGRU) model.
  • Utilized the BiGRU model for the classification of credit card fraud.

Main Results:

  • The OCSODL-CCFD technique demonstrated superior performance in detecting and classifying credit card fraud.
  • Extensive simulation analyses confirmed the model's effectiveness.
  • Comparative analysis showed significant improvements over existing methods based on various evaluation metrics.

Conclusions:

  • The proposed OCSODL-CCFD model offers a highly effective solution for credit card fraud detection.
  • The integration of OCSO for feature selection and CKHA-tuned BiGRU for classification significantly enhances detection accuracy.
  • This research contributes to securing e-payment systems against evolving fraudulent activities.