Marco Schreyer

About me

Since 2024, I’ve been a DAAD IFI Postdoc at the International Computer Science Institute (ICSI), affiliated with UC Berkeley, where my research focuses on the intersection of deep learning and financial auditing. I develop novel approaches in this domain, aiming to solve the quest for more effective financial audits through artificial intelligence techniques.

Before joining ICSI, I completed my Ph.D. at the University of St.Gallen (HSG) within the AI:ML research group, under the supervision of Damian Borth and Miklos A. Vasarhelyi. During my Ph.D., I was a visiting Swiss Mobi.Doc research fellow at the Continuous Audit and Reporting Research Lab (CARLab) at Rutgers University, from 2022 to 2023.

After graduating from the University of Mannheim, I spent nearly a decade working in the Forensic Services practice at PricewaterhouseCoopers (PwC), specializing in advanced data analytics for forensic accounting and fraud investigations.

Selected Publications

Please see my Google Scholar for a complete list.

Conference Publications

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
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]

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
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
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
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
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
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
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
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]

ArXiv and SSRN Preprints

Deep Learning Augmented Risk-Based Auditing
T. Föhr, M. Schreyer, K. Moffitt, and K.-U. Marten
Preprint at the Social Science Research Network (SSRN), 2024

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Artificial Intelligence Co-Piloted Auditing
H. Gu, M. Schreyer, K. Moffitt, and Miklos A. Vasarhelyi
Preprint at the Social Science Research Network (SSRN), 2023

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Journal 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

01/2023: Audit Data Analytics, Institute of Internal Auditors (IIA) Switzerland & University of St.Gallen (HSG), Internal Auditing Programme, view [Notebooks].
11/2022: Federated Learning in Financial Auditing, University of St.Gallen (HSG), M.Sc. in Computer Science, view [Slides] and [Notebooks].
06/2022: Deep Learning and Applications, University of St.Gallen (HSG), Global School on Empirical Research Methods, view [Notebooks].
12/2022: Artificial Intelligence in Auditing, Frankfurt School of Finance and Management, Certified Audit Data Scientist, view [Notebooks].
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

11/2022: Adversarial Learning of Deepfakes in Accounting, The 53rd World Continuous Auditing & Reporting Symposium (WCARS), Rutgers University, view [Slides].
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].
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].
11/2021: Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations, 2nd ACM International Conference on AI in Finance (ICAIF), view [Slides].
04/2021: Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Networks, Nvidia’s GPU Technology Conference (GTC), view [Slides] and [Video].
03/2021: Towards Financial Fraud Detection using Deep Learning, Hong Kong Machine Learning Meetup (HKML), view [Slides] and [Video].
02/2021: Leaking Accounting Data in Plain Sight using Deep Autoencoder Networks, AAAI Workshop on Knowledge Discovery from Unstructured Data in Finance, view [Slides].
10/2020: Learning Sampling in Financial Auditing using Vector Quantised Autoencoder Networks, 1st ACM International Conference on AI in Finance (ICAIF), view [Slides].
08/2019: Detection of Accounting Anomalies using Adversarial Autoencoder Neural Networks, 2nd KDD Workshop on Anomaly Detection in Finance, view [Slides].
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 09, 2024 (using OpenAI’s GPT-4)