Adaptive Model for Credit Card Fraud Detection

Authors

  • Imane Sadgali Laboratory of Modeling and Information Technology Faculty of sciences Ben M’SIK, University Hassan II Casablanca, Morocco
  • Naoual Sael Laboratory of Modeling and Information Technology Faculty of sciences Ben M’SIK, University Hassan II Casablanca, Morocco
  • Faouzia Benabbou Laboratory of Modeling and Information Technology Faculty of sciences Ben M’SIK, University Hassan II Casablanca, Morocco

DOI:

https://doi.org/10.3991/ijim.v14i03.11763

Keywords:

Fraud Detection, Machine-Learning, Credit Card Fraud, customer profile, transaction profile

Abstract


While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.

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Published

2020-02-28

How to Cite

Sadgali, I., Sael, N., & Benabbou, F. (2020). Adaptive Model for Credit Card Fraud Detection. International Journal of Interactive Mobile Technologies (iJIM), 14(03), pp. 54–65. https://doi.org/10.3991/ijim.v14i03.11763

Issue

Vol. 14 No. 03 (2020)

Section

Papers

License

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