In response to growing FinTech competition and the need for improved
operational efficiency, this research focuses on understanding the potential of
advanced document analytics, particularly using multimodal models, in banking
processes. We perform a comprehensive analysis of the diverse banking document
landscape, highlighting the opportunities for efficiency gains through
automation and advanced analytics techniques in the customer business. Building
on the rapidly evolving field of natural language processing (NLP), we
illustrate the potential of models such as LayoutXLM, a cross-lingual,
multimodal, pre-trained model, for analyzing diverse documents in the banking
sector. This model performs a text token classification on German company
register extracts with an overall F1 score performance of around 80\%. Our
empirical evidence confirms the critical role of layout information in
improving model performance and further underscores the benefits of integrating
image information. Interestingly, our study shows that over 75% F1 score can be
achieved with only 30% of the training data, demonstrating the efficiency of
LayoutXLM. Through addressing state-of-the-art document analysis frameworks,
our study aims to enhance process efficiency and demonstrate the real-world
applicability and benefits of multimodal models within banking.

本研究旨在通过理解先进的文档分析技术，特别是多模态模型，在银行业务的操作效率提升和应对金融科技竞争方面的潜力。通过综合分析多样化的银行文件环境，我们展示了通过自动化和高级分析技术在客户业务中提高效率的机会。借助自然语言处理领域的快速发展，我们阐明了在银行业领域分析各种文件的 LayoutXLM 等模型潜力。该模型可对德国公司注册文件进行文本标记分类，整体 F1 得分约为 80%。我们的实证证据证实了布局信息在提高模型性能方面的关键作用，并进一步强调了整合图像信息的好处。有趣的是，我们的研究表明，仅使用 30% 的训练数据就可以达到超过 75% 的 F1 得分，证明了 LayoutXLM 的高效性。通过解决最先进的文档分析框架，我们的研究旨在提高流程效率，展示多模态模型在银行业务中的实际适用性和好处。