Applying Large Language Model Analysis and Backend Web Services in Regulatory Technologies for Continuous Compliance Checks

Regulatory technologies (RegTechs) are a set of electronic and digital technologies applied to check compliance in industrial processes. Such applications also aim to simplify the process of data collection and exchange according to the expected format over the cloud or the internet. Industrial processes are required to meet basic regulatory requirements according to law and follow a set of industry practices. Industry practices must be compliant with the basic regulatory requirements. Such applications also need a high level of privacy to protect the individual participant’s data from competitors but are revealed to the relevant regulatory agencies. However, there cannot be a standard data procurement method, as the industrial processes are different for individual businesses and often involve various stages of data collection with different aims. Also, the regulatory requirements may be changed over time. These challenges can be addressed over an online system that uses large language models (LLM) to perform continuous compliance checks. With LLMs, RegTech can be easily scaled up to meet new requirements. It can also help with data analysis and reformatting for different stakeholders in RegTech, such as producers, supply chains, regulators, and financial institutions. It can check for acceptable values with regards to RegTech through either numeric comparisons or enumerations matching. In this paper, we propose a comprehensive RegTech framework backed by LLM and web services. We propose a method to measure the accuracy of LLM in returning appropriate responses for RegTech queries and herein analyze several LLMs to conclude that they are satisfactory for basic tasks, but a dedicated LLM is needed for RegTech. Furthermore, we test the LLM’s tool-calling capabilities to identify and use dedicated functions in the form of web services to enhance the analytical accuracy and consistency of RegTech-related prompts.