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A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining

Author

Listed:
  • Hamid Bekamiri

    (Business School Department, Aalborg University, Fredrik Bajers Vej 7K, 9220 Aalborg East, Denmark)

  • Seyedeh Fatemeh Ghasempour Ganji

    (Department of Management, Ferdowsi University of Mashhad, Azadi Square 9177948974, Iran)

  • Biagio Simonetti

    (Department of Law, Economics, Management and Quantitative Methods, University of Sannio, 82100 Benevento, Italy
    WSB University, 80266 Gdansk, Poland
    National Institute of Geophysics and Volcanology, INGV, 80124 Rome, Italy)

  • Seyed Amin Hosseini Seno

    (Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square 9177948974, Iran)

Abstract

As a result of changes in approach from traditional to virtual banking system, security in data exchange has become more important; thus, it seems essentially necessary to present a pattern based on smart models in order to reduce fraud in this field. A new algorithm has been provided in this article to improve security and to specify the limits of giving special services to Internet banking users in order to pave appropriate ground for virtual banking. In addition to identifying behavioral models of customers, this algorithm compares the behaviors of any customer with this model and finally computes the rate of trust in customer’s behavior. The hybrid data-mining and knowledge based structure has been adapted in this algorithm according to fuzzy systems. In this research, qualitative data was gathered from interviews with banking experts, analyzed by Expert Choice to identify the most important variables of customer behavior analysis, and to analyze customer behavior and customer bank Internet transaction data for a period of one year by MATLAB and Clementine. The results of this survey indicate that the potential of the given structure to recognize the rate of trust in Internet bank user’s behavior might be at reasonable level for experts in this area.

Suggested Citation

  • Hamid Bekamiri & Seyedeh Fatemeh Ghasempour Ganji & Biagio Simonetti & Seyed Amin Hosseini Seno, 2021. "A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining," Mathematics, MDPI, vol. 9(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:916-:d:540035
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    References listed on IDEAS

    as
    1. Mahmut Sami Öztürk & Hayrettin Usul, 2020. "Detection of Accounting Frauds Using the Rule-Based Expert Systems within the Scope of Forensic Accounting," Contemporary Studies in Economic and Financial Analysis, in: Contemporary Issues in Audit Management and Forensic Accounting, volume 102, pages 155-171, Emerald Group Publishing Limited.
    2. Sharmila Subudhi & Suvasini Panigrahi, 2020. "Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 14(3), pages 18-37, July.
    3. Wiwik Utami & Lucky Nugroho & Ratna Mappanyuki & Venny Yelvionita, 2020. "Early Warning Fraud Determinants in Banking Industries," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(6), pages 604-627, June.
    4. Hemanta Doloi, 2008. "Application of AHP in improving construction productivity from a management perspective," Construction Management and Economics, Taylor & Francis Journals, vol. 26(8), pages 841-854.
    5. Wiwik Utami & Lucky Nugroho & Ratna Mappanyuki & Venny Yelvionita, 2020. "Early Warning Fraud Determinants in Banking Industries," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(6), pages 604-627.
    6. Elena-Adriana MINASTIREANU & Gabriela MESNITA, 2019. "An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(1), pages 5-16.
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