Marco Schreyer

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About Me

Since 2024, I’m advancing the application of AI in auditing at the Swiss Federal Audit Office (SFAO). In the SFAO we design, build, and deploy novel auditing methodologies to improve the effectiveness and analytical depth of public-sector audits. We further examine AI systems used in the public administration for reliability, transparency, and compliance.

Before joining the SFAO, I conducted research at the International Computer Science Institute (ICSI) as a DAAD IFI Fellow, collaborating with Kesheng Wu and Alex Sim at Lawrence Berkeley National Laboratory and UC Berkeley on agentic audit methods and frameworks.

I completed my Ph.D. at the University of St.Gallen (HSG) in the AI:ML research group, where I worked under the supervision of Damian Borth and Miklos A. Vasarhelyi on deep learning methods for auditing and accounting. During my PhD studies, I conducted research at the Continuous Audit and Reporting Research Lab (CARLab) at Rutgers University as a visiting Swiss Mobi.Doc Research Fellow, where I developed AI-based approaches for continuous auditing and financial anomaly detection.

Earlier, I worked nearly a decade with the Forensic Services team at PricewaterhouseCoopers (PwC), applying advanced data analytics to forensic accounting, fraud investigations, and financial misconduct detection.

Institutions University of Mannheim PricewaterhouseCoopers (PwC) University of St.Gallen Rutgers University International Computer Science Institute (ICSI)
Fellowships Swiss National Science Foundation (SNSF) German Academic Exchange Service (DAAD)

Recent News

2026·07
Paper on deep-learning in auditing with T. Foehr and K.-U. Marten accepted for publication in International Journal of Accounting Information Systems (IJAIS).
2026·06
Taught the 2026 deep-learning fundamentals and applications class with D. Borth at the University of St. Gallen's GSERM Summer School.
2026·04
Paper on building AI agents in supreme audit institutions with O. Willig accepted for publication in the Swiss CPA Journal (Expert Focus).
2026·03
Paper on AI governance and the three lines model with Y. Weiser and T.F. Ruud accepted for publication in the Norwegian Magma Journal.
2026·02
Paper on graph neural networks with Q. Huang, N. R. Michiles Jr., and Miklos A. Vasarhelyi accepted for publication in A Journal of Theory and Practice (AJPT).

Selected Publications

Please see my Google Scholar for a more up to date list.

Journal Publications

Deep Learning Meets Risk-Based Auditing: A Holistic Framework for Leveraging Foundation and Task-Specific Models in Audit Procedures

Deep Learning Meets Risk-Based Auditing: A Holistic Framework for Leveraging Foundation and Task-Specific Models in Audit Procedures
T. Föhr, M. Schreyer, K. Moffitt, and K.-U. Marten
International Journal of Accounting Information Systems (IJAIS) 57, 2026
[html], [pdf]

Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries

Connecting the Dots: Graph Neural Networks for Auditing Accounting
Journal Entries
Q. Huang, M. Schreyer, N. R. Michiles Jr., and Miklos A. Vasarhelyi
AUDITING: A Journal of Practice & Theory (AJPT), 2026
[html], [pdf]

Artificial Intelligence Co-Piloted Auditing

Artificial Intelligence Co-Piloted Auditing
H. Gu, M. Schreyer, K. Moffitt, and Miklos A. Vasarhelyi
International Journal of Accounting Information Systems (IJAIS) 54, 2024
[html], [pdf]

Conference Publications

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
T. Sattarov, M. Schreyer, and D. Borth
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025
[html], [pdf]

Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular Data

Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular Data
M. Schreyer, T. Sattarov, A. Sim, and K. Wu
ACM International Conference on Artificial Intelligence in Finance (ICAIF), 2024
[html], [pdf]

FinDiff: Diffusion Models for Financial Tabular Data Generation

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]

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

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

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]

Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks

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]

Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks

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]

Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks

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]

Workshop Publications

FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation

FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
T. Sattarov, M. Schreyer, and D. Borth
AAAI Workshop on AI in Finance for Social Impact (AIFinSi), 2024
[html], [pdf], [poster]

Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing

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]

Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data

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]

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

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 (AAAI-KDF), 2021
[html], [pdf]

Adversarial Learning of Deepfakes in Accounting

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

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]

ArXiv and SSRN Preprints

Artificial Intelligence Agentic Auditing

Artificial Intelligence Agentic Auditing
H. Gu, M. Schreyer, K. Moffitt, and Miklos A. Vasarhelyi
Preprint available open-access (SSRN), 2024
[html], [pdf]

Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation

Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
T. Sattarov, M. Schreyer, and D. Borth
Preprint available open-access (arXiv), 2024
[html], [pdf]

Professional Journal Publications (in English)

Building a Future Digital Audit Workforce - Equipping artificial intelligence (AI) agents with computer aided audit tools (CAATs)

Building a Future Digital Audit Workforce - Equipping Artificial Intelligence (AI) Agents with Computer Aided Audit Tools (CAATs)
O. Willig and M. Schreyer
EXPERTsuisse, Expert Focus (04), 182-189 (Expert Focus), 2026
[html], [pdf]

Governing the Future Digital Workforce: The Three Lines Model in the Age of Agentic Artificial Intelligence

Governing the Future Digital Workforce: The Three Lines Model in the Age of Agentic Artificial Intelligence
T.F. Ruud, M. Schreyer, and Y. Weiser
Econa, Magma, 29 (1), 147–158. (Magma), 2026
[html], [pdf]

A Graph Says More Than A Thousand Journal Entries - Harnessing Graph Autoencoder Networks in Auditing

A Graph Says More Than A Thousand Journal Entries - Harnessing Graph Autoencoder Networks in Auditing
Q. Huang, M. Schreyer, N.R. Michiles, and M.A. Vasarhelyi
EXPERTsuisse, Expert Focus (12), 653-659 (Expert Focus), 2024
[html], [pdf]

Collective Artificial Intelligence in Auditing - Advancing Audit Models through Federated Learning Without Sharing Proprietary Data

Collective Artificial Intelligence in Auditing - Advancing Audit Models through Federated Learning Without Sharing Proprietary Data
M. Schreyer, D. Borth, T.F. Ruud, and M.A. Vasarhelyi
EXPERTsuisse, Expert Focus (04), 180-186 (Expert Focus), 2024
[html], [pdf]

Artificial Intelligence Enabled Audit Sampling - Learning to Draw Representative Audit Samples from Large-Scale Journal Entry Data

Artificial Intelligence Enabled Audit Sampling - Learning to Draw Representative Audit Samples from Large-Scale Journal Entry Data
M. Schreyer, A.S. Gierbl, T.F. Ruud, and D. Borth
EXPERTsuisse, Expert Focus (04), 106-112 (Expert Focus), 2022
[html], [pdf]

Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning Enabled Detection of Anomalies

Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning Enabled Detection of Anomalies
M. Schreyer, M. Baumgartner, T.F. Ruud, and D. Borth
EXPERTsuisse, Expert Focus (01), 45-50 (Expert Focus), 2022
[html], [pdf]

Professional Journal Publications (in German)

Agentische Künstliche Intelligenz in der Wirtschaftsprüfung

Agentische Künstliche Intelligenz in der Wirtschaftsprüfung
C. Kopp, M. Schreyer, and Prof. Dr. Michael Birk
WPg - Die Wirtschaftsprüfung 17, 895-902 (WPg), 2025
[non open access]

Generative Künstliche Intelligenz und Risikoorientierter Prüfungsansatz

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

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

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

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

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

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, 674-681 (WPg), 2018
[non open access]

Teaching & Lectures

Since 2021

Deep Learning Fundamentals and Applications, University of St.Gallen (HSG), GSERM Summer School on Emperical Research Methods.

Since 2023

Data Analytics & Artificial Intelligence in Auditing, Frankfurt School of Finance & Management (FS), Certified Audit Data and AI Specialist.

Since 2025

Artificial Intelligence in Internal Auditing, Zurich University of Applied Sciences (ZHAW), CAS Internal Audit and Good Governance.

Recent Presentation, Panels & Talks

2026·06·23

How New Technologies are Shaping Tomorrow’s Audits?, National Auditors’ Conference 2026, EXPERTsuisse, Bern, Switzerland.

2026·06·02

The Emergence of Agentic Artificial Intelligence in Internal Auditing, National Conference 2026, Institute of Internal Auditors (IIA), Lausanne, Switzerland.


Last updated: July 21, 2026 (using Anthropic’s Claude Code)