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Credit Card Fraud Detectıon in Retaıl Shopping Using Reinforcement Learning

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • L. SaiRamesh

    (Anna University, Department of IST, CEG Campus)

  • E. Ashok

    (Anna University, Department of IST, CEG Campus)

  • S. Sabena

    (Anna University—Regional Centre, Department of CSE)

  • A. Ayyasamy

    (Government Polytechnic College, Department of Computer Engineering)

Abstract

The increased use of network usage for performing online shopping and globalization has created a necessity of credit card usage throughout the world. Credit-card fraud has lead to considerable loss to the merchants and card users of about billions of dollars. Machine learning algorithm development paved the way for finding the fraud in more sophisticated ways but practical implementations are rarely reported. This paper proposed a method for deploying the fraud detection system which can efficiently find the fraud in the provided transaction. SARSA algorithm is used here for prediction of threshold value to detect the fraud accounts. Reinforcement algorithm is used for detecting the frauds. Finally, the benchmark analysis is performed to find the effectiveness of the proposed algorithm. The weight calculated from SARSA reinforcement learning algorithm is given to the random forest method to increase the accuracy the prediction of credit card defaulters.

Suggested Citation

  • L. SaiRamesh & E. Ashok & S. Sabena & A. Ayyasamy, 2020. "Credit Card Fraud Detectıon in Retaıl Shopping Using Reinforcement Learning," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1541-1549, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_158
    DOI: 10.1007/978-3-030-41862-5_158
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