Prof. Dr.-Ing.

Bauckhage, Christian

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Now showing 1 - 4 of 4
  • Publication
    Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models
    ( 2023-12)
    Deußer, Tobias orcid-logo
    ;
    Leonhard, David
    ;
    Hillebrand, Lars Patrick
    ;
    Berger, Armin
    ;
    Khaled, Mohamed
    ;
    Heiden, Sarah
    ;
    Dilmaghani, Tim
    ;
    Kliem, Bernd
    ;
    Loitz, Rüdiger
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Correct identification and correction of contradictions and inconsistencies within financial reports constitute a fundamental component of the audit process. To streamline and automate this critical task, we introduce a novel approach leveraging large language models and an embedding-based paragraph clustering methodology. This paper assesses our approach across three distinct datasets, including two annotated datasets and one unannotated dataset, all within a zero-shot framework. Our findings reveal highly promising results that significantly enhance the effectiveness and efficiency of the auditing process, ultimately reducing the time required for a thorough and reliable financial report audit.
  • Publication
    Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models
    ( 2023-08-22)
    Hillebrand, Lars Patrick
    ;
    Berger, Armin
    ;
    Deußer, Tobias orcid-logo
    ;
    Dilmaghani, Tim
    ;
    Khaled, Mohamed
    ;
    Kliem, B.
    ;
    Loitz, Rüdiger
    ;
    Pielka, Maren
    ;
    Leonhard, David
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.
  • Publication
    KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
    ( 2022-12)
    Deußer, Tobias orcid-logo
    ;
    Ali, Syed Musharraf
    ;
    Hillebrand, Lars Patrick
    ;
    Nurchalifah, Desiana Dien
    ;
    Jacob, Basil
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where the main objective is to extract Key Performance Indicators (KPIs) from financial documents and link them to their numerical values and other attributes. We further provide four accompanying baselines for benchmarking potential future research. Additionally, we propose a new way of measuring the success of said extraction process by incorporating a word-level weighting scheme into the conventional F 1 score to better model the inherently fuzzy borders of the entity pairs of a relation in this domain.
  • Publication
    Anonymization of German financial documents using neural network-based language models with contextual word representations
    ( 2022-03)
    Biesner, David
    ;
    Ramamurthy, Rajkumar
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    Loitz, Rüdiger
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    Lübbering, Max
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    Hillebrand, Lars Patrick
    ;
    Ladi, Anna
    ;
    Stenzel, Robin
    ;
    Pielka, Maren
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    The automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques.