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Anomaly Detection and Categorization for a Data Quality Management Framework in Financial Regulatory Reporting

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Enterprise Design, Operations, and Computing. EDOC 2023 Workshops (EDOC 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 498))

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Abstract

Financial institutions are subject to stringent regulatory reporting requirements to manage operational risk in international financial markets. Producing accurate and timely reports has raised challenges in current data processes of big data heterogeneity, system interoperability and enterprise-wide management. Data quality management is a key concern, with current approaches being time-consuming, expensive, and risky. This research proposes to design, develop, and evaluate a Financial Reporting Data Quality Framework that allows non-IT data consumers to contextualize data observations. The framework will use anomaly algorithms to detect and categorize observations as genuine business activities or data quality issues. To ensure sustainability and ongoing relevance, the framework will also embed an update mechanism.

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Authors and Affiliations

  1. University of New South Wales, Sydney, NSW, 2052, Australia

    Aya Tafech

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  1. Aya Tafech
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Correspondence to Aya Tafech .

Editor information

Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Tiago Prince Sales

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Sybren de Kinderen

  3. Vienna University of Technology, Vienna, Austria

    Henderik A. Proper

  4. Technical University of Munich, Heilbronn, Germany

    Luise Pufahl

  5. University of Groningen, Groningen, The Netherlands

    Dimka Karastoyanova

  6. University of Twente, Enschede, The Netherlands

    Marten van Sinderen

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Tafech, A. (2024). Anomaly Detection and Categorization for a Data Quality Management Framework in Financial Regulatory Reporting. In: Sales, T.P., de Kinderen, S., Proper, H.A., Pufahl, L., Karastoyanova, D., van Sinderen, M. (eds) Enterprise Design, Operations, and Computing. EDOC 2023 Workshops . EDOC 2023. Lecture Notes in Business Information Processing, vol 498. Springer, Cham. https://doi.org/10.1007/978-3-031-54712-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-54712-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54711-9

  • Online ISBN: 978-3-031-54712-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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