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Using Process Mining to Reduce Fraud in Digital Onboarding

Author

Listed:
  • Matheus Camilo da Silva

    (Dipartimento di Ingegneria e Architettura, Università Degli Studi di Trieste, 34127 Trieste, Italy
    These authors contributed equally to this work.)

  • Gabriel Marques Tavares

    (Dipartimento di Informatica, Università Degli Studi di Milano Statale, 20122 Milano, Italy
    These authors contributed equally to this work.)

  • Marcos Cesar Gritti

    (Banco Bari Research Data and Development, Curitiba 80250205, Brazil)

  • Paolo Ceravolo

    (Dipartimento di Informatica, Università Degli Studi di Milano Statale, 20122 Milano, Italy
    These authors contributed equally to this work.)

  • Sylvio Barbon Junior

    (Dipartimento di Ingegneria e Architettura, Università Degli Studi di Trieste, 34127 Trieste, Italy)

Abstract

In the context of online banking, new users have to register their information to become clients through mobile applications; this process is called digital onboarding. Fraudsters often commit identity fraud by impersonating other people to obtain access to banking services by using personal data obtained illegally and causing damage to the organisation’s reputation and resources. Detecting fraudulent users by their onboarding process is not a trivial task, as it is difficult to identify possible vulnerabilities in the process to be exploited. Furthermore, the modus operandi for differentiating the behaviour of fraudulent actors and legitimate users is unclear. In this work, we propose the usage of a process mining (PM) approach to detect identity fraud in digital onboarding using a real fintech event log. The proposed PM approach is capable of modelling the behaviour of users as they go through a digital onboarding process, while also providing insight into the process itself. The results of PM techniques and the machine learning classifiers showed a promising 80% accuracy rate in classifying users as fraudulent or legitimate. Furthermore, the application of process discovery in the event log dataset produced an insightful visual model of the onboarding process.

Suggested Citation

  • Matheus Camilo da Silva & Gabriel Marques Tavares & Marcos Cesar Gritti & Paolo Ceravolo & Sylvio Barbon Junior, 2023. "Using Process Mining to Reduce Fraud in Digital Onboarding," FinTech, MDPI, vol. 2(1), pages 1-18, February.
    • Handle: RePEc:gam:jfinte:v:2:y:2023:i:1:p:9-137:d:1083712
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    References listed on IDEAS

    as
    1. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    2. Jans, Mieke & Alles, Michael & Vasarhelyi, Miklos, 2013. "The case for process mining in auditing: Sources of value added and areas of application," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 1-20.
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