Regulatory Technology (RegTech) is an emerging set of computing and network-based information systems and practices intended to enhance and improve regulatory compliance processes. Such technologies rely on collecting exclusive information from the environment and humans through automated Internet of Things (IoT) sensors and self-reported data. The key enablers of RegTech are the increased capabilities and reduced cost of IoT and Artificial Intelligence (AI) technologies. This article focuses on a survey of RegTech, highlighting the recent developments in various sectors. This work identifies the characteristics of existing implementations of RegTech applications in the financial industry. It examines the critical features that non-financial industries such as agriculture must address when using such technologies. We investigate the suitability of existing technologies applied in financial sectors to other industries and the potential gaps to be filled between them in terms of designing information systems for regulatory frameworks. This includes identifying specific operational parameters that are key differences between the financial and non-financial sectors that can be supported with IoT and AI technologies. These can be used by both producers of goods and services and regulators who need an affordable and efficient supervision method for managing relevant organizations.

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.

Compliance checks are a critical aspect of any agricultural production and supply chain. These comprise a chain of activities and responsibilities shared by producers and regulators. Currently, the responsibility for collecting and processing data primarily lies with the regulators. Regulators are the primary users of regulatory technologies, while producers, such as farmers, have limited capabilities. This has been due to a lack of reliable hardware and software technology that can be deployed at the producer’s sites. However, recent advancements in Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), and cloud computing have significantly increased reliability and reduced deployment costs. This paper reviews the regulatory landscape of agriculture, regulatory technologies, IIoT, and their feasibility for producers, using a new Technology Readiness Framework for Compliance, specifically designed for Regulatory Technologies (RegTech) and regulation compliance. It classifies technologies into three categories: Early Stage, Emerging, and Established. It concludes that most essential agricultural issues already have mature technological solutions within the scope of IIoT that producers can use directly without supervision, while still maintaining the integrity and validity of the data. The paper discusses and measures the maturity of IIoT and other electronic and digital technologies for integration into RegTech. This categorization offers a new perspective on technology readiness and lays the foundation for future RegTech platform design, e.g., by decentralizing and empowering producers with reliable technologies.

Rules form the foundation of regulatory systems by defining the obligations, permissions, and prohibitions that govern human activities. Regulatory processes universally rely on taking specific inputs from the stakeholders before checking them against specific rules. As regulators in various sectors such as agriculture increasingly adopt digital regulatory technologies, regulatory rules must be interpreted consistently by both humans and machines. Among the diverse rule types in legislation, quantitative rules are particularly important because they define measurable constraints that, if modified, may alter the scope of regulatory compliance. This paper presents a large language model (LLM)-based framework for analyzing rule relaxation in quantitative agricultural regulations. The proposed approach classifies quantitative rules into three categories: single-value rules, ranged or interval rules, and ratio rules. A mathematical framework is introduced to formally represent quantitative rule relaxation, while controlled numerical perturbations are applied to determine whether modifications to regulatory parameters expand, reduce, or ambiguously affect the set of actions permitted by a regulation. This methodology addresses the semantic effects of quantitative changes on deontic regulatory rules while preserving their underlying obligations and permissions. The framework is evaluated using pesticide regulations published by the Australian Pesticides and Veterinary Medicines Authority (APVMA). Performance is assessed using both class- and rule-specific accuracy and macro F1 scores for multiple LLM models. The results demonstrate that LLMs can be used to interpret the semantic consequences of quantitative modifications. However, the accuracy of performance declines with the number of variables and the complexity of the rules. The proposed framework establishes a practical foundation for explainable agricultural Regulatory Technology (RegTech) systems and future Rules as Code implementations.