Thesis project: Detecting unusual journal entries in financial statement audits with auto-encoder neural networks

Every once in a while, a new news item appears reporting a new case of fraud and the many affected victims. According to the Association of Certified Fraud Examiners, fraud has caused more than $7 billion in total losses in 125 different countries between January 2016 and October 2017.  The current fraud detection techniques are based on heuristics and past experience, so the main issue is that new types of fraud cannot be detected. The thesis aims to introduce a new method for fraud detection which resolves the downsides of the current methods, namely auto encoder neural networks. This method is explored by first realizing a replication study, upon which adversarial auto encoders are implemented to attempt to exceed the results of the existing studies.

At the moment Joyce Hendriks ( is working on this topic.