MSc Project defence (30 ECTS) - Department of Computer Defence - Hafsteinn Atli Stefánsson.
Title: Unsupervised Anomaly Detection in Financial Transactions.
Data and time: 31st of May at 10:00 Room: M 104
Abstract:
The aim of the project is to devise a process which can detect anomalies in vendor ledger entries for Össur hf. Due to the data being unlabeled two unsupervised machine learning model architectures, Isolation forest & Autoencoder, are utilized. Various pre-processing methods such as PCA, clustering and feature engineering are tested on the ledger entry data which is fed into each model. The models are evaluated through manual review of the anomalies returned by each model, resulting in an iterative model building process with a human-in-the-loop. Additionally, the models are explained using Shapley values in order to gather insights into which features contribute the most to classifying ledger entries as anomalous or regular. Lastly, the models are set up to run on Össur's Azure Machine Learning environment in order to facilitate future model development. The final result being an end-to-end machine learning solution to detect anomalies which can be refined and iterated upon based on manual reviews conducted by auditors within Össur hf.
Committee members:
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María Óskarsdóttir, Associate Professor, RU, Supervisor
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Stephan Schiffel, Assistant Professor, RU, Examiner
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Björn Rafn Gunnarsson, post-doc, KU Leuven, Examiner
