Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models
Tobias Deußer, David Leonhard, Lars Hillebrand, Armin Berger, M. Ben Khaled, Sarah Heiden, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Christian Bauckhage, Rafet Sifa · 2023
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.