| Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks M Schreyer, T Sattarov, D Borth, A Dengel, B Reimer arXiv preprint arXiv:1709.05254, 2017 | 126 | 2017 |
| Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks M Schreyer, T Sattarov, C Schulze, B Reimer, D Borth KDD 2019 Workshop on Anomaly Detection in Finance, 2019 | 41 | 2019 |
| Adversarial Learning of Deepfakes in Accounting M Schreyer, T Sattarov, B Reimer, D Borth NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness …, 2019 | 36 | 2019 |
| Evaluation of Graylevel-features for Printing Technique Classification in High-throughput Document Management Systems C Schulze, M Schreyer, A Stahl, T Breuel Computational Forensics, 35-46, 2008 | 36 | 2008 |
| Using DCT Features for Printing Technique and Copy Detection C Schulze, M Schreyer, A Stahl, T Breuel Advances in Digital Forensics V, 95-106, 2009 | 30 | 2009 |
| Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination. M Schreyer, C Schulze, A Stahl, W Effelsberg Informatiktage 8, 39-42, 2009 | 26 | 2009 |
| Automatic Counterfeit Protection System Code Classification J Van Beusekom, M Schreyer, TM Breuel Media Forensics and Security, 75410F, 2010 | 16 | 2010 |
| Artificial Intelligence Co-Piloted Auditing H Gu, M Schreyer, K Moffitt, MA Vaserhelyi SSRN preprint SSRN:4444763, 2023 | 14 | 2023 |
| Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks M Schreyer, T Sattarov, D Borth Proceedings of the International Conference on Artificial Intelligence …, 2021 | 12 | 2021 |
| Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks M Schreyer, T Sattarov, AS Gierbl, B Reimer, D Borth Proceedings of the International Conference on Artificial Intelligence …, 2020 | 12 | 2020 |
| Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits M Schreyer, T Sattarov, D Borth Proceedings of the Third ACM International Conference on AI in Finance, 105-113, 2022 | 10 | 2022 |
| Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data H Hemati, M Schreyer, D Borth AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience …, 2021 | 6 | 2021 |
| FinDiff: Diffusion Models for Financial Tabular Data Generation T Sattarov, M Schreyer, D Borth Proceedings of the Fourth ACM International Conference on AI in Finance, 64-72, 2023 | 5 | 2023 |
| RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations R Müller, M Schreyer, T Sattarov, D Borth Proceedings of the Third ACM International Conference on AI in Finance, 174-182, 2022 | 5 | 2022 |
| Assuring Sustainable Futures: Auditing Sustainability Reports using AI Foundation Models TL Föhr, M Schreyer, TA Juppe, KU Marten Available at SSRN 4502549, 2023 | 4 | 2023 |
| Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing M Schreyer, H Hemati, D Borth, MA Vasarhelyi FL-NeurIPS'22 International Workshop on Federated Learning: Recent Advances …, 2022 | 4 | 2022 |
| Artificial Intelligence Enabled Audit Sampling-Learning to draw representative and interpretable audit samples from large-scale journal entry data M Schreyer, AS Gierbl, F Ruud, D Borth EXPERTSuisse, Expert Focus, 106-112, 2022 | 3 | 2022 |
| Künstliche Intelligenz in der Prüfungspraxis-Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen AS Gierbl, M Schreyer, P Leibfried, D Borth EXPERTSuisse, Expert Focus, 612-617, 2020 | 3 | 2020 |
| Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten AS Gierbl, M Schreyer, DS Borth, P Leibfried Zeitschrift für Internationale Rechnungslegung (IRZ) 2021 (7/8), 349-355, 2021 | 2 | 2021 |
| Deep Learning Meets Risk-Based Auditing: A Holistic Framework for Leveraging Foundation and Task-Specific Models in Audit Procedures TL Föhr, M Schreyer, K Moffitt, KU Marten Available at SSRN 4488271, 2023 | 1 | 2023 |
Damian BorthProfessor of Artificial Intelligence & Machine Learning, University of St. GallenVerified email at unisg.ch
