Master Thesis Defense on "Fraud detection in online payments using Spark ML" (Monday, Aug 28, 1pm)

Student:  Ignacio Amaya
Date and Time:  1:00pm, Monday, 28th August 2017
Place: Ada room, 4th floor
Examiner:  Šarūnas Girdzijauskas
Supervisor:  Vladimir Vlassov
Title: Fraud detection in online payments using Spark ML
Opponent: Adrián Ramírez

Abstract:
Fraudulent online payments cause large amount of losses, so companies build fraud detection systems to prevent them.
In this thesis we study how machine learning can improve those systems.
Previous academic work have failed to address fraud detection in real-world datasets using distributed computing frameworks, which are needed due to their big data volume.
To fill this gap, we have used real-world payment data to build a fraud detection classifier on Spark ML. Class imbalance and non-stationarity reduced the performance of our models, so experiments to tackle those problems have been performed.
Our best results are obtained combining undersampling and oversampling on the training data. Keeping only the newest data and ensembling several models with different majority class instances also improve the predictions.
A final model has been deployed at Qliro, an important online payments provider in the Nordics, enhancing their fraud detection system and helping investigators catch frauds that were being missed before.