Comparative assessment of Machine Learning methods for fraud detection and improving the interpretability of the best model

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases. This research project presents a machine learning classifier for fraud detection that will predict if the transaction is fraudulent or not, and also the interpretation of the predictions made by the model for them to be understood by humans.

Faculty Supervisor:

Lourdes Peña-Castillo


Kratika Naskulwar


Verafin Inc.


Computer science


Information and communications technologies


Memorial University of Newfoundland



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