1 code implementation • 11 Jan 2024 • Timur Sattarov, Marco Schreyer, Damian Borth
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and healthcare.
1 code implementation • 4 Sep 2023 • Timur Sattarov, Marco Schreyer, Damian Borth
The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations.
no code implementations • 21 Sep 2022 • Timur Sattarov, Dayananda Herurkar, Jörn Hees
We find that denoising autoencoders applied to this task already outperform other approaches in the cell error detection rates as well as in the expected value rates.
no code implementations • 19 Sep 2022 • Ricardo Müller, Marco Schreyer, Timur Sattarov, Damian Borth
However, in unsupervised DL as often applied in financial audits, these methods explain the model output at the level of encoded variables.
no code implementations • 26 Aug 2022 • Marco Schreyer, Timur Sattarov, Damian Borth
In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients.
no code implementations • 23 Sep 2021 • Marco Schreyer, Timur Sattarov, Damian Borth
International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries.
no code implementations • 6 Aug 2020 • Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, Damian Borth
The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'.
no code implementations • 9 Oct 2019 • Marco Schreyer, Timur Sattarov, Bernd Reimer, Damian Borth
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.
4 code implementations • 2 Aug 2019 • Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, Damian Borth
We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries.
4 code implementations • 15 Sep 2017 • Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, Bernd Reimer
Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations.