IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v2y2023i1p9-137d1083712.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/2/1/9/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2674-1032/2/1/9/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Werner, Michael, 2017. "Financial process mining - Accounting data structure dependent control flow inference," International Journal of Accounting Information Systems, Elsevier, vol. 25(C), pages 57-80.
    2. Ruhnke, Klaus, 2023. "Empirical research frameworks in a changing world: The case of audit data analytics," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 51(C).
    3. Gray, Glen L. & Debreceny, Roger S., 2014. "A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 15(4), pages 357-380.
    4. Koreff, Jared & Weisner, Martin & Sutton, Steve G., 2021. "Data analytics (ab) use in healthcare fraud audits," International Journal of Accounting Information Systems, Elsevier, vol. 42(C).
    5. Sudhanshu Singh & Rakesh Verma & Saroj Koul, 2022. "A collaborative method for simultaneous operations: case of an eye clinic," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 711-731, June.
    6. Mahama, Habib & Elbashir, Mohamed Z. & Sutton, Steve G. & Arnold, Vicky, 2016. "A further interpretation of the relational agency of information systems: A research note," International Journal of Accounting Information Systems, Elsevier, vol. 20(C), pages 16-25.
    7. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    8. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    9. Werner, Michael & Wiese, Michael & Maas, Annalouise, 2021. "Embedding process mining into financial statement audits," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    10. Jans, Mieke & Hosseinpour, Marzie, 2019. "How active learning and process mining can act as Continuous Auditing catalyst," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 44-58.
    11. Daehyoun Choi & Hind R’bigui & Chiwoon Cho, 2021. "Candidate Digital Tasks Selection Methodology for Automation with Robotic Process Automation," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    12. Sledgianowski, Deb & Gomaa, Mohamed & Tan, Christine, 2017. "Toward integration of Big Data, technology and information systems competencies into the accounting curriculum," Journal of Accounting Education, Elsevier, vol. 38(C), pages 81-93.
    13. Krieger, Felix & Drews, Paul & Velte, Patrick, 2021. "Explaining the (non-) adoption of advanced data analytics in auditing: A process theory," International Journal of Accounting Information Systems, Elsevier, vol. 41(C).
    14. Pall Rikhardsson & Kishore Singh & Peter Best, 2019. "Exploring Continuous Auditing Solutions and Internal Auditing: A Research Note," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 18(4), pages 614-639, December.
    15. Kumar, Satish & Marrone, Mauricio & Liu, Qi & Pandey, Nitesh, 2020. "Twenty years of the International Journal of Accounting Information Systems: A bibliometric analysis," International Journal of Accounting Information Systems, Elsevier, vol. 39(C).
    16. Zerbino, Pierluigi & Stefanini, Alessandro & Aloini, Davide, 2021. "Process Science in Action: A Literature Review on Process Mining in Business Management," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    17. Chen, Qianqian & Chen, Zhi, 2024. "Mandatory internal control audit and corporate financialization," Finance Research Letters, Elsevier, vol. 62(PA).
    18. Sudhanshu Singh & Rakesh Verma & Saroj Koul, 2022. "A data-driven approach to shared decision-making in a healthcare environment," OPSEARCH, Springer;Operational Research Society of India, vol. 59(2), pages 732-746, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jfinte:v:2:y:2023:i:1:p:9-137:d:1083712. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.