The Association of Certified Fraud Examiners estimates in its “GlobalStudy on Occupational Fraud and Abuse 2018” that organizations lose 5% of their annual revenues due to fraud. To detect potentially fraudulent activities international audit standards require the direct assessment of such journal entries. The detection of traces of fraud in up to several hundred million journal entries remains a labor-intensive task re-quiring significant time effort and resources. To overcome this challenge we propose the application of Adversarial Autoencoder Neural Networks (AAEs).
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