"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:1c523b11-c220-43da-b4c2-a712d6fee8d4","http://resolver.tudelft.nl/uuid:1c523b11-c220-43da-b4c2-a712d6fee8d4","Automated Transaction Monitoring","Kostense, Bastijn (TU Delft Electrical Engineering, Mathematics and Computer Science); Hageman, Rico (TU Delft Electrical Engineering, Mathematics and Computer Science); van der Wilk, Hilco (TU Delft Electrical Engineering, Mathematics and Computer Science); van Walraven, Bram (TU Delft Electrical Engineering, Mathematics and Computer Science)","van den Oever, Sander (mentor); Visser, Otto (graduation committee); Wang, Huijuan (graduation committee); Delft University of Technology (degree granting institution)","2019","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.","Transaction Monitoring; Fraud; Machine Learning","en","bachelor thesis","","","","","","","","","","","","","",""
"uuid:0f78c996-59d9-48b0-9280-243644811117","http://resolver.tudelft.nl/uuid:0f78c996-59d9-48b0-9280-243644811117","Automated Pricing Suggestions","Katzy, Jonathan (TU Delft Electrical Engineering, Mathematics and Computer Science); Rietveld, Tim (TU Delft Electrical Engineering, Mathematics and Computer Science); van der Steeg, Jaap-Jan (TU Delft Electrical Engineering, Mathematics and Computer Science); Wiegel, Erik (TU Delft Electrical Engineering, Mathematics and Computer Science)","van Riemsdijk, Birna (mentor); Wang, Huijuan (graduation committee); Dorresteijn, Stefan (graduation committee); Bloo, Roel (graduation committee); Jonker, Catholijn (mentor); Delft University of Technology (degree granting institution)","2018","As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time.
After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.