Automated Transaction Monitoring

Bachelor Thesis (2019)
Author(s)

B. Kostense (TU Delft - Electrical Engineering, Mathematics and Computer Science)

R. Hageman (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Hilco van der Wilk (TU Delft - Electrical Engineering, Mathematics and Computer Science)

B. van Walraven (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

S.Y. van den Oever – Mentor (TU Delft - Computer Science & Engineering-Teaching Team)

OW Visser – Graduation committee member (TU Delft - Computer Science & Engineering-Teaching Team)

H. Wang – Graduation committee member (TU Delft - Multimedia Computing)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2019 Bastijn Kostense, Rico Hageman, Hilco van der Wilk, Bram van Walraven
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Bastijn Kostense, Rico Hageman, Hilco van der Wilk, Bram van Walraven
Graduation Date
03-07-2019
Awarding Institution
Delft University of Technology
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

For the past 10 weeks, we have been tasked with improving the performance of the transaction monitoring system of bunq, an internationally active mobile bank. bunq has requested that we improve this system by automating the training of the machine learning model, providing better input data for this model and creating additional machine learning models. During this project, we have been working at the offices of bunq on this system. This thesis will give an overview of our research, software design process and implementation.

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