## 3 Digitalization of Compliance Checks with RegTech LLM
### Design Requirements of the Digital Regulatory Systems
#### 3.1.3 Industry Expectations  
In many compliance scenarios, businesses need to make quick decisions. In today's fast-paced business environment, the ability to quickly adapt to changing regulations and  
Figure 2: AI in RegTech Application. respond to compliance challenges can be a significant competitive advantage. Delays in compliance can lead to costly fines, reputational damage, and operational disruptions. Responsive RegTech solutions can help businesses minimize these risks. In terms of human factors, LLM can resolve the two biggest problems with respect to compliance. A key issue with compliance is having the knowledge of the requirements at the right time. While it is possible for expert workers to remember the most common rules and record the data accordingly in a timely manner, it is still difficult to remember everything. This problem becomes more complex if the compliance is checked by a non-expert or if the rules are relaxed/changed for a particular process cycle. The other issue is on-demand analysis. Often, the producers may want to try new things by analyzing their own past performance and potentially stimulating new deals to see if they would still meet the competition. However, if they do not have an expert on-site, such analysis would require additional costs. LLM can help with this by providing a comprehensive guide to all the rules, both base and temporary. The two main features are as follows:  
* _Responsiveness_: RegTech LLM should be ready to answer in a real-time chat, such as in ChatGPT. It can respond in seconds with a human-friendly output, thus allowing users to receive fast advice. The producers do not need to wait for expensive expert advice;
* _On-demand analysis_: Existing compliance software can provide fixed compliance check features. LLM can give flexibility in the questions users can ask. This means an analysis can be fine-tuned to specific user situations and requirements. LLM has to be capable of the following: Step 1. Understanding the question. Step 2. Determining the procedure to obtain the results. Step 3. Performing the analysis according to step 2. Step 4. Producing a human-friendly output. In this context, the evaluation of the RegTech LLM is different from that of conventional LLM applications. 1. The _inputs_, i.e., the prompts, need to be succinct and short, as human users are not expected to explain too much. This can be addressed by considering only 0-shot prompts; 2. The _outputs_ must satisfy the question's requirements. A majority of RegTech questions and their corresponding data will be discrete. This means the output will contain a countable number of distinct elements (\(\epsilon\)), and thus, if any of these elements (\(m\)) are missed by LLM or if new extra elements are added to the output (\(e\)), it will result in an incorrect response from the LLM. As such, we define the _output accuracy_ (\(\lambda\)) of a RegTech LLM response as follows: \[\lambda=\frac{\epsilon-m}{\epsilon}\times\frac{\epsilon}{\epsilon+e}=\frac{ \epsilon-m}{\epsilon+e}\ \ldots\] (1) where \(\epsilon\) can be lists of strings, sets of rows from a time series dataset, sets of rules, sets of web service functions for selection, or sets of format specifications. Variations in the outputs do not matter for RegTech LLM. If the same information is represented in multiple formats, it does not create any problems for the target users. Ultimately, with respect to RegTech, from an industry perspective, RegTech LLM is expected to achieve the following targets:
* _Product safety_: This is related to the safety of using or consuming the industrial process's product. For the agriculture sector, this means adhering to stringent food safety regulations, including Hazard Analysis Critical Control Point (HACCP) principles, to prevent foodborne illnesses and protect public health;* _Environmental protection_: This is about quantifying and reducing the environmental impact of the industrial process.