Marco Schreyer
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Schreyer
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Marco
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marco.schreyer@unisg.ch
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+41 71 224 79 13
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A SUM GREATER THAN ITS PARTS: COLLECTIVE ARTIFICIAL INTELLIGENCE IN AUDITING - Advancing Audit Models through Federated Learning Without Sharing Proprietary Data

2024-04-10 , Marco Schreyer , Damian Borth , Tore Flemming Ruud , Miklos A. Vasarhelyi

Artificial intelligence exhibits the potential to transform auditing by extracting insights from large volumes of audit-relevant data. This article introduces federated learning, an emerging artificial intelligence learning setting. It outlines the integration of federated learning into practical audit procedures to gather collective intelligence from various audit-relevant data sources while ensuring data privacy.

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Artificial Intelligence Enabled Audit Sampling - Learning to draw representative and interpretable audit samples from large-scale journal entry data

2022-03-07 , Schreyer, Marco , Gierbl, Anita Stefanie , Ruud, Flemming , Borth, Damian

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Künstliche Intelligenz in der Prüfungspraxis - Eine Bestandsaufnahme aktueller Einsatzmöglichkeiten und Herausforderungen

2020-09-01 , Gierbl, Anita Stefanie , Schreyer, Marco , Leibfried, Peter , Borth, Damian

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

2022-02-28 , Hemati, Hamed , Schreyer, Marco , Borth, Damian

International audit standards require the direct assessment of a financial statement’s underlying accounting journal entries. Driven by advances in artificial intelligence, deep-learning inspired audit techniques emerged to examine vast quantities of journal entry data. However, in regular audits, most of the proposed methods are applied to learn from a comparably stationary journal entry population, e.g., of a financial quarter or year. Ignoring situations where audit relevant distribution changes are not evident in the training data or become incrementally available over time. In contrast, in continuous auditing, deep-learning models are continually trained on a stream of recorded journal entries, e.g., of the last hour. Resulting in situations where previous knowledge interferes with new information and will be entirely overwritten. This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences. The framework is evaluated based on deliberately designed audit scenarios and two real-world datasets. Our experimental results provide initial evidence that such a learning scheme offers the ability to reduce false-positive alerts and false-negative decisions.

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Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning enabled Detection of Anomalies in Financial Accounting Data

2022-01-07 , Schreyer, Marco , Baumgartner, Marcel , Ruud, Flemming , Borth, Damian

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Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis

2022-02-07 , Schreyer, Marco , Gierbl, Anita Stefanie , Ruud, Flemming , Borth, Damian

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Künstliche Intelligenz in der Wirtschaftsprüfung - Identifikation ungewöhnlicher Buchungen in der Finanzbuchhaltung

2018-11-01 , Schreyer, Marco , Sattarov, Timur , Borth, Damian , Dengel, Andreas , Reimer, Bernd

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Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien in der Revisionspraxis

2022-01-07 , Schreyer, Marco , Baumgartner, Marcel , Ruud, Flemming , Borth, Damian

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Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten

2021-07-28 , Gierbl, Anita Stefanie , Schreyer, Marco , Borth, Damian , Leibfried, Peter

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FinDiff: Diffusion Models for Financial Tabular Data Generation

2023 , Timur Sattarov , Marco Schreyer , Damian Borth , Timur Sattarov

The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations. These challenges often hinder the ability of both academics and practitioners to conduct collaborative research effectively. The emergence of generative models, particularly diffusion models, capable of synthesizing data mimicking the underlying distributions of real-world data presents a compelling solution. This work introduces 'FinDiff', a diffusion model designed to generate real-world financial tabular data for a variety of regulatory downstream tasks, for example economic scenario modeling, stress tests, and fraud detection. The model uses embedding encodings to model mixed modality financial data, comprising both categorical and numeric attributes. The performance of FinDiff in generating synthetic tabular financial data is evaluated against state-of-the-art baseline models using three real-world financial datasets (including two publicly available datasets and one proprietary dataset). Empirical results demonstrate that FinDiff excels in generating synthetic tabular financial data with high fidelity, privacy, and utility.

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