Natural Language Processing for Financial Regulation
This work addresses a domain-specific challenge in financial regulation by providing a method for semantic search when labeled data is scarce, though it appears incremental in nature.
The paper tackled the problem of semantic matching between financial rules and policies without supervised datasets, achieving improved performance over pre-trained sentence-transformers using freely available resources.
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.