Marco Schreyer
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Schreyer
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Marco
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  • Publication
    FinDiff: Diffusion Models for Financial Tabular Data Generation
    (Association for Computing Machinery (ACM), 2023)
    Timur Sattarov
    ;
    Marco Schreyer orcid-logo
    ;
    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.
    Type: conference paper
    Journal: 4th ACM International Conference on AI in Finance
    URL: https://dl.acm.org/doi/abs/10.1145/3604237.3626876
    DOI: 10.1145/3604237.3626876
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118776
    Scopus© Citations 1
  • Publication
    Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data
    (Association for the Advancement of Artificial Intelligence (AAAI), 2022-02-28)
    Hemati, Hamed
    ;
    Schreyer, Marco orcid-logo
    ;
    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.
    Type: conference paper
    URL: https://sites.google.com/view/aaaiwfs2022
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108930
  • Publication
    Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing
    ( 2022-12-02)
    Schreyer, Marco orcid-logo
    ;
    Hemati, Hamed
    ;
    Borth, Damian
    ;
    Vasarhelyi, Miklos A.
    The International Standards on Auditing (ISA) require auditors to collect reasonable assurance that financial statements are free of material misstatement. At the same time, a central objective of Continuous Assurance is the real-time assessment of digital accounting journal entries. Recently, driven by the advances in artificial intelligence, Deep Learning techniques have emerged in financial auditing to examine vast quantities of accounting data. However, learning highly adaptive audit models in decentralized and dynamic settings remains challenging. It requires the study of data distribution shifts over multiple clients and time periods. In this work, we propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity. Our empirical results, using real-world datasets and combined federated-continual learning strategies, demonstrate the learned model's ability to detect anomalies in audit settings of data distribution shifts.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108007
  • Publication
    Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
    (Association for Computing Machinery (ACM), 2022-11-01)
    Schreyer, Marco orcid-logo
    ;
    Sattarov, Timur
    ;
    Borth, Damian
    The ongoing 'digital transformation' fundamentally changes audit evidence's nature, recording, and volume. Nowadays, the International Standards on Auditing (ISA) requires auditors to examine vast volumes of a financial statement's underlying digital accounting records. As a result, audit firms also `digitize' their analytical capabilities and invest in Deep Learning (DL), a successful sub-discipline of Machine Learning. The application of DL offers the ability to learn specialized audit models from data of multiple clients, e.g., organizations operating in the same industry or jurisdiction. In general, regulations require auditors to adhere to strict data confidentiality measures. At the same time, recent intriguing discoveries showed that large-scale DL models are vulnerable to leaking sensitive training data information. Today, it often remains unclear how audit firms can apply DL models while complying with data protection regulations. In this work, we propose a Federated Learning framework to train DL models on auditing relevant accounting data of multiple clients. The framework encompasses Differential Privacy and Split Learning capabilities to mitigate data confidentiality risks at model inference. Our results provide empirical evidence that auditors can benefit from DL models that accumulate knowledge from multiple sources of proprietary client data.
    Type: conference paper
    URL: https://dl.acm.org/doi/abs/10.1145/3533271.3561674
    DOI: 10.1145/3533271.3561674
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108108
    Scopus© Citations 5
  • Publication
    Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
    (Association for Computing Machinery (ACM), 2021-11-03)
    Marco Schreyer orcid-logo
    ;
    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 5
  • Publication
    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 orcid-logo
    ;
    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
  • Publication
    Learning Sampling in Financial Statement Audits using Vector Quantised Variational Autoencoder Neural Networks
    (Association of Computing Machinery (ACM), 2020-10-01)
    Schreyer, Marco orcid-logo
    ;
    Timur Sattarov
    ;
    Gierbl, Anita Stefanie
    ;
    Reimer, Bernd
    ;
    Borth, Damian
    ;
    Marco Schreyer orcid-logo
    The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement ('true and fair presentation'). International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such journal entries, auditors regularly conduct an 'audit sampling' i.e. a sample-based assessment of a subset of these journal entries. However, the task of audit sampling is often conducted early in the overall audit process, where the auditor might not be aware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the use of a Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks to learn a representation of journal entries able to provide a comprehensive 'audit sampling' to the auditor. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.
    Type: conference paper
    Journal: Proceedings of the First ACM International Conference on AI in Finance
    URL: https://dl.acm.org/doi/10.1145/3383455.3422546
    DOI: 10.1145/3383455.3422546
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/113091
    Scopus© Citations 5
  • Publication
    Adversarial Learning of Deepfakes in Accounting
    (Cornell University - arXiv, 2019-12-13)
    Schreyer, Marco orcid-logo
    ;
    Sattarov, Timur
    ;
    Reimer, Bernd
    ;
    Borth, Damian
    Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international audit standards demand auditors to directly assess journal entries using 'Computer Assisted AuditTechniques' (CAATs). At the same time, discoveries in deep learning research revealed that machine learning models are vulnerable to 'adversarial attacks'. It also became evident that such attack techniques can be misused to generate 'Deepfakes' designed to directly attack the perception of humans by creating convincingly altered media content. The research of such developments and their potential impact on the finance and accounting domain is still in its early stage. We believe that it is of vital relevance to investigate how such techniques could be maliciously misused in this sphere. In this work, we show an adversarial attack against CAATs using deep neural networks. We first introduce a real-world 'thread model' designed to camouflage accounting anomalies such as fraudulent journal entries. Second, we show that adversarial autoencoder neural networks are capable of learning a human interpretable model of journal entries that disentangles the entries latent generative factors. Finally, we demonstrate how such a model can be maliciously misused by a perpetrator to generate robust 'adversarial' journal entries that mislead CAATs.
    Type: conference paper
    URL: https://arxiv.org/abs/1910.03810
    DOI: 10.48550/arXiv.1910.03810
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/97949
  • Publication
    Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
    ( 2019-08-05)
    Schreyer, Marco orcid-logo
    ;
    Sattarov, Timur
    ;
    Schulze, Christian
    ;
    Reimer, Bernd
    ;
    Borth, Damian
    The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. In contrast, more advanced approaches inspired by the recent success of deep learning often lack seamless interpretability of the detected results. To overcome this challenge, we propose the application of adversarial autoencoder networks. We demonstrate that such artificial neural networks are capable of learning a semantic meaningful representation of real-world journal entries. The learned representation provides a holistic view on a given set of journal entries and significantly improves the interpretability of detected accounting anomalies. We show that such a representation combined with the networks reconstruction error can be utilized as an unsupervised and highly adaptive anomaly assessment. Experiments on two datasets and initial feedback received by forensic accountants underpinned the effectiveness of the approach.
    Type: conference paper
    URL: https://arxiv.org/abs/1908.00734
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/98358
  • Publication
    Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Neural Networks
    (Cornell University - arXiv, 2018-08-01)
    Schreyer, Marco orcid-logo
    ;
    Sattarov, Timur
    ;
    Borth, Damian
    ;
    Dengel, Andreas
    ;
    Reimer, Bernd
    ;
    Marco Schreyer orcid-logo
    Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning, we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
    Type: conference paper
    DOI: 10.48550/arXiv.1709.05254
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/100689