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。

银行流程自动化的多模态文档分析

Multimodal Document Analytics for Banking Process Automation

Multimodal pre-training with text, layout, and image has achieved SOTA
performance for visually-rich document understanding tasks recently, which
demonstrates the great potential for joint learning across different
modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model
for multilingual document understanding, which aims to bridge the language
barriers for visually-rich document understanding. To accurately evaluate
LayoutXLM, we also introduce a multilingual form understanding benchmark
dataset named XFUND, which includes form understanding samples in 7 languages
(Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and
key-value pairs are manually labeled for each language. Experiment results show
that the LayoutXLM model has significantly outperformed the existing SOTA
cross-lingual pre-trained models on the XFUND dataset. The pre-trained
LayoutXLM model and the XFUND dataset are publicly available at
this https URL

本文提出了一种基于 LayoutXLM 的多模态预训练模型，用于多语言文档理解，并在名为 XFUND 的多语言表单理解基准数据集上进行了验证，结果表明 LayoutXLM 模型在跨语言预训练方面优于现有的 SOTA 模型。