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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    Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
    (Association for Computing Machinery (ACM), 2021-11-03)
    Marco Schreyer  
    ;
    Timur Sattarov  
    ;
    Damian Borth  
    International audit standards require the direct assessment of a financial statement’s underlying accounting transactions, referred to as journal entries. Recently, driven by the advances in artificial intelligence, deep learning inspired audit techniques have emerged in the field of auditing vast quantities of journal entry data. Nowadays, the majority of such methods rely on a set of specialized models, each trained for a particular audit task. At the same time, when conducting a financial statement audit, audit teams are confronted with (i) challenging time-budget constraints, (ii) extensive documentation obligations, and (iii) strict model interpretability requirements. As a result, auditors prefer to harness only a single preferably ‘multi-purpose’ model throughout an audit engagement. We propose a contrastive self-supervised learning framework designed to learn audit task invariant accounting data representations to meet this requirement. The framework encompasses deliberate interacting data augmentation policies that utilize the attribute characteristics of journal entry data. We evaluate the framework on two real-world datasets of city payments and transfer the learned representations to three downstream audit tasks: anomaly detection, audit sampling, and audit documentation. Our experimental results provide empirical evidence that the proposed framework offers the ability to increase the efficiency of audits by learning rich and interpretable ‘multi-task’ representations.
    Type:conference paper
    URL:https://dl.acm.org/doi/10.1145/3490354.3494373
    DOI:10.1145/3490354.3494373
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109758
    Scopus© Citations 10
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    Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks
    (Association for the Advancement of Artificial Intelligence (AAAI), 2021-02-09)
    Schreyer, Marco  
    ;
    Schulze, Christian
    ;
    Borth, Damian  
    Nowadays, organizations collect vast quantities of sensitive information in 'Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master data, or strategic sales price information. The leakage of such information poses a severe threat for companies as the number of incidents and the reputational damage to those experiencing them continue to increase. At the same time, discoveries in deep learning research revealed that machine learning models could be maliciously misused to create new attack vectors. Understanding the nature of such attacks becomes increasingly important for the (internal) audit and fraud examination practice. The creation of such an awareness holds in particular for the fraudulent data leakage using deep learning-based steganographic techniques that might remain undetected by state-of-the-art 'Computer Assisted Audit Techniques' (CAATs). In this work, we first introduce a real-world 'threat model' designed to leak sensitive accounting data. Second, we show that a deep steganographic process, constituted by three neural networks, can be trained to hide such data in unobtrusive 'day-to-day' images. Finally, we provide qualitative and quantitative evaluations on two publicly available real-world payment datasets.
    Type:conference paper
    URL:https://aaai-kdf.github.io/kdf2021/
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110659