Master Assignment
Unsupervised outlier detection in financial statement audits
Type: Master M-BIT
Location: University of Twente
Period: Mar, 2018 - Oct, 2019
Student: Lenderink, R.J. (Rick, Student M-BIT)
Date Final project: October 7, 2019
Supervisors:
Abstract:
During financial statement audits great amounts of transactional data is examined by auditing accountants to provide assurance that an organization’s financial statements are reported in accordance with relevant accounting principles. This thesis focuses on the application unsupervised outlier detection techniques to aid auditors in finding outlying journal entries that could be of interest in terms of fraud or errors made. In order to conduct fraud, one has to deviate from ’normal’ behaviour and from regular financial transaction patterns. The same can be said about errors made in financial administrations, erroneous transactions are rare and deviate from a regular transaction pattern. It is therefore believed that there is a link between abnormal journal entries (outliers / anomalies) and financial fraud or financial errors.