Deep Fraud Detection

Financial fraud is a serious issue that is taking place globally and causing considerable damage at great expense. Statistical analysis and machine learning tools can help financial institutions detect different types of fraud. In some cases however, mislabeling and the cost of classification may actually increase the volume of ‘false positives’ for supervised methods. As the number of normal transactions in financial domains far outweigh the number of anomalous transactions, it is challenging to classify the anomaly labels. In this research project, a combination of semi-supervised and unsupervised Deep Learning methods will be applied to detect outliers from different perspectives.

Faculty Supervisor:

Hamid Usefi


Majid Afshar


Verafin Inc.


Computer science


Finance, insurance and business


Memorial University of Newfoundland



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