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Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

Authors:

Marco Schreyer,

Timur Sattarov,

Damian BorthAuthors Info & Claims

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

Pages 105 - 113

https://doi.org/10.1145/3533271.3561674

Published: 26 October 2022 Publication History

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Abstract

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.

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Cited By

Barna LIonescu BHurducaci Gorea C(2024)Professional Skills of Future Accountants Working in a Digitized Environment Dominated by ERP Systems or Artificial IntelligenceProceedings of the International Conference on Business Excellence10.2478/picbe-2024-010718:1(1290-1305)Online publication date: 3-Jul-2024
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Luzón MRodríguez-Barroso NArgente-Garrido AJiménez-López DMoyano JDel Ser JDing WHerrera F(2024)A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected TrendsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12421511:4(824-850)Online publication date: Apr-2024
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Index Terms

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
      2. Unsupervised learning
        Dimensionality reduction and manifold learning
2. Information systems
  1. Information systems applications
    1. Enterprise information systems
      1. Enterprise resource planning

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cover image ACM Other conferences

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

November 2022

527 pages

ISBN:9781450393768

DOI:10.1145/3533271

Editors:
Daniele Magazzeni
J.P. Morgan AI Research
,
Senthil Kumar
Capital One
,
Rahul Savani
University of Liverpool
,
Renyuan Xu
University of Southern California
,
Carmine Ventre
King's College London
,
Blanka Horvath
University of Oxford
,
Ruimeng Hu
University of California Santa Barbara
,
Tucker Balch
J.P. Morgan AI Research
,
Francesca Toni
Imperial College London

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Published: 26 October 2022

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ICAIF '22: 3rd ACM International Conference on AI in Finance

November 2 - 4, 2022

NY, New York, USA

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Cited By

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https://doi.org/10.2478/picbe-2024-0107
Chen CWang KLi PSakurai K(2024)A Multi-Head Federated Continual Learning Approach for Improved Flexibility and Robustness in Edge EnvironmentsInternational Journal of Networking and Computing10.15803/ijnc.14.2_12314:2(123-144)Online publication date: 2024
https://doi.org/10.15803/ijnc.14.2_123
Luzón MRodríguez-Barroso NArgente-Garrido AJiménez-López DMoyano JDel Ser JDing WHerrera F(2024)A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected TrendsIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12421511:4(824-850)Online publication date: Apr-2024
https://doi.org/10.1109/JAS.2024.124215
Narula MMeena JVishwakarma D(2024)Dynamic Resource-Aware Federated Framework for Secure and Sustainable Learning in Data Sensitive Applications2024 IEEE 9th International Conference for Convergence in Technology (I2CT)10.1109/I2CT61223.2024.10543390(1-8)Online publication date: 5-Apr-2024
https://doi.org/10.1109/I2CT61223.2024.10543390
Vanmathi CFarouk AAlhammad SMangayarkarasi RBhattacharya SKasyapa M(2024)The Role of Blockchain in Transforming Industries Beyond FinanceIEEE Access10.1109/ACCESS.2024.346861112(148845-148867)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3468611
Narula MMeena JVishwakarma D(2024)A comprehensive review on Federated Learning for Data-Sensitive ApplicationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108128133:PBOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108128
Chen CLiu JTan HLi XWang KLi PSakurai KDou D(2024)Trustworthy federated learning: privacy, security, and beyondKnowledge and Information Systems10.1007/s10115-024-02285-2Online publication date: 26-Nov-2024
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Deußer TLeonhard DHillebrand LBerger AKhaled MHeiden SDilmaghani TKliem BLoitz RBauckhage CSifa R(2023)Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386673(2814-2822)Online publication date: 15-Dec-2023
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