Using Process Mining to Reduce Fraud in Digital Onboarding

Matheus Camilo da Silva, Gabriel Marques Tavares, Marcos Cesar Gritti, Paolo Ceravolo and Sylvio Barbon Junior ()
Additional contact information
Matheus Camilo da Silva: Dipartimento di Ingegneria e Architettura, Università Degli Studi di Trieste, 34127 Trieste, Italy
Gabriel Marques Tavares: Dipartimento di Informatica, Università Degli Studi di Milano Statale, 20122 Milano, Italy
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
Sylvio Barbon Junior: Dipartimento di Ingegneria e Architettura, Università Degli Studi di Trieste, 34127 Trieste, Italy

FinTech, 2023, vol. 2, issue 1, 1-18

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.

Keywords: digital onboarding; fraud detection; predictive process monitoring; fintech (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2674-1032/2/1/9/pdf (application/pdf)
https://www.mdpi.com/2674-1032/2/1/9/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jfinte:v:2:y:2023:i:1:p:9-137:d:1083712

Access Statistics for this article

FinTech is currently edited by Ms. Lizzy Zhou

More articles in FinTech from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().