Prof. Dr.-Ing.

Bauckhage, Christian

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  • Publication
    Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models
    ( 2023-12)
    Deußer, Tobias orcid-logo
    ;
    Leonhard, David
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    Hillebrand, Lars Patrick
    ;
    Berger, Armin
    ;
    Khaled, Mohamed
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    Heiden, Sarah
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    Dilmaghani, Tim
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    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
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    Kliem, B.
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    Loitz, Rüdiger
    ;
    Pielka, Maren
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    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
    Contradiction Detection in Financial Reports
    ( 2023-01-23)
    Deußer, Tobias orcid-logo
    ;
    Pielka, Maren
    ;
    Pucknat, Lisa
    ;
    Jacob, Basil
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    Dilmaghani, Tim
    ;
    Nourimand, Mahdis
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    Kliem, Bernd
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    Loitz, Rüdiger
    ;
    Bauckhage, Christian
    ;
    Sifa, Rafet
    Finding and amending contradictions in a financial report is crucial for the publishing company and its financial auditors. To automate this process, we introduce a novel approach that incorporates informed pre-training into its transformer-based architecture to infuse this model with additional Part-Of-Speech knowledge. Furthermore, we fine-tune the model on the public Stanford Natural Language Inference Corpus and our proprietary financial contradiction dataset. It achieves an exceptional contradiction detection F1 score of 89.55% on our real-world financial contradiction dataset, beating our several baselines by a considerable margin. During the model selection process we also test various financial-document-specific transformer models and find that they underperform the more general embedding approaches.