Detecting and explaining potential financial fraud cases in invoice data with Machine Learning

This research looks into the different possibilities of using unsupervised outlier detection algorithms to detect potential fraud cases in invoice data of the public sector. Next, it researches possible explanation mechanisms to explain the process and outcomes of the algorithm. This research designs and validates an explanation facility for financial auditors and identifies opportunities for using this facility.