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.
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.