This article provides an understanding of Natural Language Processing
techniques in the framework of financial regulation, more specifically in order
to perform semantic matching search between rules and policy when no dataset is
available for supervised learning. We outline how to outperform simple
pre-trained sentences-transformer models using freely available resources and
explain the mathematical concepts behind the key building blocks of Natural
Language Processing.

本文介绍了自然语言处理技术在金融监管框架中的应用，特别是在没有可用于监督学习的数据集时进行规则和政策的语义匹配搜索。我们概述了如何通过使用免费资源超越简单的预训练句子变换模型，并解释了自然语言处理的关键组成部分背后的数学概念。

金融监管的自然语言处理

Natural Language Processing for Financial Regulation

The increasing availability of granular and big data on various objects of
interest has made it necessary to develop methods for condensing this
information into a representative and intelligible map. Financial regulation is
a field that exemplifies this need, as regulators require diverse and often
highly granular data from financial institutions to monitor and assess their
activities. However, processing and analyzing such data can be a daunting task,
especially given the challenges of dealing with missing values and identifying
clusters based on specific features.
To address these challenges, we propose a variant of Lloyd's algorithm that
applies to probability distributions and uses generalized Wasserstein
barycenters to construct a metric space which represents given data on various
objects in condensed form. By applying our method to the financial regulation
context, we demonstrate its usefulness in dealing with the specific challenges
faced by regulators in this domain. We believe that our approach can also be
applied more generally to other fields where large and complex data sets need
to be represented in concise form.

本文提出一种基于 Wasserstein barycenters 的 Lloyd 算法变体，可应用于概率分布，用于构建表示给定数据的度量空间，以应对金融监管领域中的具体挑战，从而使大而复杂的数据集能以简洁的形式呈现。

金融机构的几何形态 —— 金融数据的 Wasserstein 聚类

The geometry of financial institutions -- Wasserstein clustering of  financial data

With the explosive growth of transaction activities in online payment
systems, effective and realtime regulation becomes a critical problem for
payment service providers. Thanks to the rapid development of artificial
intelligence (AI), AI-enable regulation emerges as a promising solution. One
main challenge of the AI-enabled regulation is how to utilize multimedia
information, i.e., multimodal signals, in Financial Technology (FinTech).
Inspired by the attention mechanism in nature language processing, we propose a
novel cross-modal and intra-modal attention network (CIAN) to investigate the
relation between the text and transaction. More specifically, we integrate the
text and transaction information to enhance the text-trade jointembedding
learning, which clusters positive pairs and push negative pairs away from each
other. Another challenge of intelligent regulation is the interpretability of
complicated machine learning models. To sustain the requirements of financial
regulation, we design a CIAN-Explainer to interpret how the attention mechanism
interacts the original features, which is formulated as a low-rank matrix
approximation problem. With the real datasets from the largest online payment
system, WeChat Pay of Tencent, we conduct experiments to validate the practical
application value of CIAN, where our method outperforms the state-of-the-art
methods.

本文介绍一种基于人工智能技术的在线支付系统监管方案，用跨模态和内模态关注网络（CIAN）来研究文本和交易之间的关系，并提出 CIAN-Explainer 来解释机器学习模型的注意力机制如何作用于原始特征。实验证明，该方法在实际应用中具有价值和优越的性能表现。