Prof. Dr.-Ing.

Bauckhage, Christian

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Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models

2023-12 , Deußer, Tobias , 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.

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Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models

2023-08-22 , Hillebrand, Lars Patrick , Berger, Armin , Deußer, Tobias , 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.

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Publication

Contradiction Detection in Financial Reports

2023-01-23 , Deußer, Tobias , Pielka, Maren , Pucknat, Lisa , Jacob, Basil , Dilmaghani, Tim , Nourimand, Mahdis , Kliem, Bernd , 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.

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