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Marco Schreyer
Marco Schreyer
International Computer Science Institute (ICSI), Berkeley
Verified email at icsi.berkeley.edu - Homepage
Title
Cited by
Cited by
Year
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
1232017
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
412019
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
352019
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
352008
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
302009
Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination.
M Schreyer, C Schulze, A Stahl, W Effelsberg
Informatiktage 8, 39-42, 2009
262009
Automatic Counterfeit Protection System Code Classification
J Van Beusekom, M Schreyer, TM Breuel
Media Forensics and Security, 75410F, 2010
152010
Artificial Intelligence Co-Piloted Auditing
H Gu, M Schreyer, K Moffitt, MA Vaserhelyi
SSRN preprint SSRN:4444763, 2023
132023
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
122021
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
92022
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
52022
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
52021
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
42023
Assuring Sustainable Futures: Auditing Sustainability Reports using AI Foundation Models
TL Föhr, M Schreyer, TA Juppe, KU Marten
Available at SSRN 4502549, 2023
42023
Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-Learning Enabled Detection of Anomalies in Financial Accounting Data
M Schreyer, M Baumgartner, TF Ruud, D Borth
Expert Focus 1, 45-50, 2022
42022
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
32022
Künstliche Intelligenz in der Prüfungspraxis-Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen
AS Gierbl, M Schreyer, P Leibfried, D Borth
Expert Focus 2020 (09), 612-617, 2020
22020
Künstliche Intelligenz in der Wirtschaftsprüfung-Identifikation ungewöhnlicher Buchungen in der Finanzbuchhaltung
M Schreyer, T Sattarov, D Borth, A Dengel, B Reimer
WPg-Die Wirtschaftsprüfung 72 (11), 674-681, 2018
22018
Deep Learning Meets Risk-Based Auditing: a Holistic Framework for Leveraging Foundation and Task-Specific Models in Audit Procedures
TL Föhr, KU Marten, M Schreyer
Available at SSRN 4488271, 2023
12023
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