This session will explore how auditors can be misguided or "fooled" by adversarial accounting records or adversarial financial transactions. Recent discoveries in deep learning research revealed that learned models are vulnerable to "adversarial examples," or a sample of slightly modified input data that intends to cause a human and/or machine to misclassify it. Such examples exhibit the potential to be dangerous, since they could be specifically designed to misguide auditors or an accountant. Securing accounting information systems against such "attacks" can be difficult. In this talk, we'll explain why such "adversarial examples" are of vital relevance in the context of fraud detection and financial statement audits. We will demonstrate how autoencoder neural networks can be trained in an adversarial setup to generate "fake" accounting records or financial transactions. Such financial transactions might be misused to "attack" an organization's internal control system or obfuscate fraudulent activities. The training of such examples was conducted by training several adversarial autoencoders using NVIDIA's DGX-1 system.