Hello, I’m Marco.
I’m currently a PhD student at the University of St.Gallen (HSG), Switzerland, in the AI:ML research group, working under the supervision of Damian Borth and Miklos A. Vasarhelyi. I spent 2022-2023 as a visiting Swiss Mobi.Doc research fellow at the Continuous Audit and Reporting Research Lab (CARLab) at Rutgers University, USA. Before pursuing my PhD, I worked in the Forensic Services practice at PricewaterhouseCoopers (PwC) from 2008 to 2017.
Recent News
- 09/2023: Paper accepted for the ACM ICAIF 2023 Conference in Brooklyn, USA.
- 06/2023: Accepted talk for the IFC Workshop on Data Science in Central Banking in Rom, Italy.
- 03/2023: Accepted talk for the 56th World Continuous Auditing Symposium in Madrid, Spain.
- 02/2023: Our work got featured in a Frankfurt School "Was ist Deep Learning?" video.
- 11/2022: Paper accepted for the NeurIPS Federated Learning Workshop in New Orleans, USA.
- 10/2022: Accepted talk for the 53th World Continuous Auditing Symposium in Newark, USA.
Selected Publications
Please, also see the full list at Google Scholar.
Publications in English
FinDiff: Diffusion Models for Financial Tabular Data Generation
T. Sattarov, M. Schreyer, and D. Borth
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2023
[html], [pdf]
Artificial Intelligence Co-Piloted Auditing
H. Gu, M. Schreyer, K. Moffitt, and Miklos A. Vasarhelyi
Preprint at the Social Science Research Network (SSRN), 2023
[html], [pdf]
Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
M. Schreyer, H. Hemati, D. Borth, and Miklos A. Vasarhelyi
NeurIPS Workshop on Federated Learning (NeurIPS-FL), 2022
[html], [pdf], [poster]
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
M. Schreyer, T. Sattarov, and D. Borth
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2022
[html], [pdf]
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
R. Mueller, M. Schreyer, T. Sattarov, and D. Borth
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2022
[html], [pdf]
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
H. Hemati, M. Schreyer, and D. Borth
AAAI Workshop on AI in Financial Services (AAAI-WFS), 2022
[html], [pdf]
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
M. Schreyer, T. Sattarov, and D. Borth
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2021
[html], [pdf]
Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks
M. Schreyer, C. Schulze, and D. Borth
AAAI Workshop on KD in Financial Services (AAA-KDF), 2021
[html], [pdf]
Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks
M. Schreyer, T. Sattarov, A. Gierbl, B. Reimer, and D. Borth
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2020
[html], [pdf]
Adversarial Learning of Deepfakes in Accounting
M. Schreyer, T. Sattarov, B. Reimer, and D. Borth
NeurIPS Workshop on Robust AI in Financial Services (NeurIPS), 2019
[html], [pdf]
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
M. Schreyer, T. Sattarov, C. Schulze, B. Reimer, and D. Borth
KDD Workshop on Anomaly Detection in Finance (KDD), 2019
[html], [pdf]
Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks
M. Schreyer, T. Sattarov, D. Borth, A. Dengel, and B. Reimer
Nvidia’s GPU Technology Conference (GTC), 2018
[html], [pdf]
Publications in German
Generative Künstliche Intelligenz und Risikoorientierter Prüfungsansatz
T. L. Föhr, K.-U. Marten, and M. Schreyer
Der Betrieb, Nr. 30, 1681-1693, 2023
[non open access]
Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen
M. Schreyer, A. S. Gierbl, T.F. Ruud, and D. Borth
EXPERTsuisse, Expert Focus (02), 10-18 (Expert Focus), 2022
[html], [pdf]
Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien
M. Schreyer, M. Baumgartner, T.F. Ruud, and D. Borth
EXPERTsuisse, Expert Focus (01), 39-44 (Expert Focus), 2022
[html], [pdf]
Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten
A. S. Gierbl, M. Schreyer, P. Leibfried, and D. Borth
Zeitschrift für Internationale Rechnungslegung (07/08), 349-355 (IRZ), 2021
[non open access]
Künstliche Intelligenz in der Prüfungspraxis - Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen
A. S. Gierbl, M. Schreyer, P. Leibfried, and D. Borth
EXPERTsuisse, Expert Focus (09), 612-617 (Expert Focus), 2020
[html], [pdf]
Künstliche Intelligenz in der Wirtschaftsprüfung - Identifikation ungewöhnlicher Buchungen in der Finanzbuchhaltung
M. Schreyer, T. Sattarov, D. Borth, A. Dengel, and B. Reimer
WPg - Die Wirtschaftsprüfung 72 (11), 674-681 (WPg), 2018
[non open access]
Invited Teaching & Guest Lectures
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01/2023: Audit Data Analytics, Institute of Internal Auditors (IIA) Switzerland & University of St.Gallen (HSG), Internal Auditing Programme, view [Notebooks].
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11/2022: Federated Learning in Financial Auditing, University of St.Gallen (HSG), M.Sc. in Computer Science, view [Slides] and [Notebooks].
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06/2022: Deep Learning and Applications, University of St.Gallen (HSG), Global School on Empirical Research Methods, view [Notebooks].
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12/2022: Artificial Intelligence in Auditing, Frankfurt School of Finance and Management, Certified Audit Data Scientist, view [Notebooks].
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04/2022: Applying Artificial Intelligence in Internal Audit Analytics, BI Norwegian Business School, Seminar GRC & Internal Audit in Switzerland, view [Slides] and [Notebooks].
Conference Presentations & Invited Talks
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11/2022: Adversarial Learning of Deepfakes in Accounting, The 53rd World Continuous Auditing & Reporting Symposium (WCARS), Rutgers University, view [Slides].
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11/2022: Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits, 3rd ACM International Conference on AI in Finance (ICAIF), view [Slides].
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08/2022: Deep Learning in Financial Auditing, Summer 2022 Weekly Technology Forum, Rutgers University, view [Slides] and [Video 1], [Video 2], [Video 3], [Video 4], [Video 5].
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11/2021: Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations, 2nd ACM International Conference on AI in Finance (ICAIF), view [Slides].
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04/2021: Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Networks, Nvidia’s GPU Technology Conference (GTC), view [Slides] and [Video].
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03/2021: Towards Financial Fraud Detection using Deep Learning, Hong Kong Machine Learning Meetup (HKML), view [Slides] and [Video].
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02/2021: Leaking Accounting Data in Plain Sight using Deep Autoencoder Networks, AAAI Workshop on Knowledge Discovery from Unstructured Data in Finance, view [Slides].
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10/2020: Learning Sampling in Financial Auditing using Vector Quantised Autoencoder Networks, 1st ACM International Conference on AI in Finance (ICAIF), view [Slides].
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08/2019: Detection of Accounting Anomalies using Adversarial Autoencoder Neural Networks, 2nd KDD Workshop on Anomaly Detection in Finance, view [Slides].
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04/2019: Creation of Adversarial Accounting Records to Attack Financial Statement Audits, Nvidia’s GPU Technology Conference (GTC), view [Slides] and [Video].
Last updated: March 18, 2023 (using OpenAI’s GPT-4)