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Found 4 papers, 2 papers with code
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'.
Adversarial Learning of Deepfakes in Accounting
Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors.
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries.
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations.
