Profile Page Marco Schreyer

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1. Schreyer, Marco; Hemati, Hamed; Borth, Damian & Vasarhelyi, Miklos A.: Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing. 2022. - Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). - New Orleans, LA, USA.
2. Schreyer, Marco; Sattarov, Timur & Borth, Damian: Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. 2022. - 3rd ACM International Conference on Artificial Intelligence in Finance (ICAIF). - New York City, USA.
3. Schreyer, Marco; Gierbl, Anita Stefanie; Ruud, Flemming & Borth, Damian (2022) Artificial Intelligence Enabled Audit Sampling - Learning to draw representative and interpretable audit samples from large-scale journal entry data. Expert Focus, (04). 106-112.
4. Hemati, Hamed; Schreyer, Marco & Borth, Damian: Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data. 2022. - AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance. - Virtual.
5. Schreyer, Marco; Gierbl, Anita Stefanie; Ruud, Flemming & Borth, Damian (2022) Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis. Expert Focus, (02). 10-18.
6. Schreyer, Marco; Baumgartner, Marcel; Ruud, Flemming & Borth, Damian (2022) Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning enabled Detection of Anomalies in Financial Accounting Data. Expert Focus, Special: Internal Audit (01). 39-44.
7. Schreyer, Marco; Baumgartner, Marcel; Ruud, Flemming & Borth, Damian (2022) Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien in der Revisionspraxis. Expert Focus, Special: Interne Revision (01). 39-44.
8. Schreyer, Marco; Sattarov, Timur & Borth, Damian: Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. 2021. - ACM International Conference on Artificial Intelligence in Finance (ICAIF). - London, United Kingdom.
9. Gierbl, Anita Stefanie; Schreyer, Marco; Borth, Damian & Leibfried, Peter (2021) Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten. Zeitschrift für Internationale Rechnungslegung (IRZ), (7/8). 349-355. ISSN 1862-5533
10. Schreyer, Marco; Schulze, Christian & Borth, Damian: Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks. 2021. - AAAI 2021 Workshop on Knowledge Discovery from Unstructured Data in Financial Services. - Virtual.

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8	Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks	1004
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