Marco Schreyer

412 total citations
13 papers, 109 citations indexed

About

Marco Schreyer is a scholar working on Artificial Intelligence, Accounting and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marco Schreyer has authored 13 papers receiving a total of 109 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 4 papers in Accounting and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Marco Schreyer's work include Imbalanced Data Classification Techniques (4 papers), Financial Distress and Bankruptcy Prediction (4 papers) and Stock Market Forecasting Methods (3 papers). Marco Schreyer is often cited by papers focused on Imbalanced Data Classification Techniques (4 papers), Financial Distress and Bankruptcy Prediction (4 papers) and Stock Market Forecasting Methods (3 papers). Marco Schreyer collaborates with scholars based in Switzerland, Germany and United States. Marco Schreyer's co-authors include Miklos A. Vasarhelyi, Damian Borth, Kevin Moffitt, Thomas M. Breuel, Joost van Beusekom, Kesheng Wu, Ullrich Köthe and Alex Sim and has published in prestigious journals such as International Journal of Accounting Information Systems, eScholarship (California Digital Library) and Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).

In The Last Decade

Marco Schreyer

12 papers receiving 104 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Marco Schreyer Switzerland 7 37 35 24 22 15 13 109
Varun Dogra India 6 78 2.1× 9 0.3× 8 0.3× 21 1.0× 8 0.5× 13 143
Li Du China 8 82 2.2× 3 0.1× 20 0.8× 22 1.0× 14 0.9× 28 158
Melanie Lourens South Africa 6 24 0.6× 6 0.2× 6 0.3× 9 0.4× 3 0.2× 79 133
Xiaojie Mao United States 6 40 1.1× 4 0.1× 18 0.8× 35 1.6× 3 0.2× 13 141
Steve Welch United States 5 20 0.5× 4 0.1× 16 0.7× 16 0.7× 4 0.3× 12 68
Kijpokin Kasemsap Thailand 6 8 0.2× 5 0.1× 17 0.7× 9 0.4× 5 0.3× 15 72
Shan Chen China 6 37 1.0× 3 0.1× 12 0.5× 9 0.4× 6 0.4× 26 88
Dhanya Jothimani Canada 7 25 0.7× 8 0.2× 51 2.1× 82 3.7× 2 0.1× 13 157
Anuj Mahajan India 5 63 1.7× 5 0.1× 10 0.4× 35 1.6× 6 0.4× 14 94
Vipula Rawte United States 5 78 2.1× 9 0.3× 5 0.2× 9 0.4× 4 0.3× 12 129

Countries citing papers authored by Marco Schreyer

Since Specialization
Citations

This map shows the geographic impact of Marco Schreyer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marco Schreyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Schreyer more than expected).

Fields of papers citing papers by Marco Schreyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Schreyer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marco Schreyer. The network helps show where Marco Schreyer may publish in the future.

Co-authorship network of co-authors of Marco Schreyer

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Schreyer. A scholar is included among the top collaborators of Marco Schreyer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marco Schreyer. Marco Schreyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Schreyer, Marco, et al.. (2024). Artificial intelligence co-piloted auditing. International Journal of Accounting Information Systems. 54. 100698–100698. 15 indexed citations
2.
Schreyer, Marco, et al.. (2024). Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries. SSRN Electronic Journal. 3 indexed citations
3.
Schreyer, Marco, et al.. (2024). Imb-FinDiff: Conditional Diffusion Models for Class Imbalance Synthesis of Financial Tabular Data. eScholarship (California Digital Library). 617–625.
4.
Schreyer, Marco, et al.. (2023). Artificial Intelligence Co-Piloted Auditing. SSRN Electronic Journal. 26 indexed citations
5.
Schreyer, Marco, et al.. (2023). Assuring Sustainable Futures: Auditing Sustainability Reports using AI Foundation Models. SSRN Electronic Journal. 8 indexed citations
6.
Schreyer, Marco, et al.. (2023). Deep Learning Augmented Risk-Based Auditing. SSRN Electronic Journal. 2 indexed citations
7.
Schreyer, Marco, et al.. (2023). FinDiff: Diffusion Models for Financial Tabular Data Generation. 64–72. 13 indexed citations
8.
Schreyer, Marco, et al.. (2022). Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits. 105–113. 10 indexed citations
9.
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11.
Beusekom, Joost van, Marco Schreyer, & Thomas M. Breuel. (2010). Automatic counterfeit protection system code classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7541. 75410F–75410F. 6 indexed citations
12.
13.
Köthe, Ullrich, et al.. (1998). ERSO-acquisition, reconstruction and simulation of real objects. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 52. 2419–2424 vol.4. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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