## 3 Digitalization of Compliance Checks with RegTech LLM
### Key Components of LLM Compliance Checks--Automation Functionalities Needed by LLM  
For streamlining the compliance check process, several types of functionalities must be provided by AI and/or LLM, with or without the use of external dedicated services:  
1. _Numeric checks_: These checks are arithmetic and logical checks to determine if the recorded numeric values are within acceptable thresholds. In the context of agriculture, environmental and production data are often recorded as time series. Typically, if IIoT is implemented, the time series is generated by devices and thus records time. If IIoT is not implemented, and the data are recorded manually, the time series may be more incomplete or less orderly. However, the aim would be to continuously monitor the recorded numeric value to check if it is within the acceptable range;
2. _Milestone checks_: This is a special case of numeric checks that check against a specific date. These checks are meant to confirm that the expert event has happened within the documented timeline. The events are not expected to be continuous but to be within time limits. Milestone checks are when we expect the event to happen within time, and compliance fails if the event does not occur by that time;
3. _List checks_: A key aspect of compliance checks is labeling. This is obviously in the form of strings, and list checks would involve logically comparing the data entered with the expected values from the list. It is essential for users to record their data using pre-approved terms to ensure verifiability;
4. _Flag/binary/fault checks_: Sometimes, unexpected events occur and must be flagged immediately. Unforeseen events do not inherently mean a compliance failure, as they can be remedied within a time limit. These checks are typically binary, i.e., whether there is an error or not, for example, whether there was a pest on the farm. Apart from the above four, there is a more complex form of compliance checks when the compliance data involve more than numeric lists and date/time data. 5. _Document check_: This is checking the format, relevance, and correctness of data entered in each section and pocket of data. It is invariably composed of list checks and could also have flag checks;
6. _Process checks_: These higher-level checks ensure the correctness of the whole industrial process by analyzing smaller chunks of data. This would involve checking for events that should occur in an expected sequence or for missing data;
7. _Image and video checks_: LLMs have multimodal capabilities to interpret and describe visual data by analyzing a given image (and possibly videos). These models, when integrated with vision modules, can process and analyze images to identify objects, recognize patterns, detect anomalies, and provide descriptive captions or insights. For RegTech LLM, computer vision algorithms are already used for visual inspection of several production-related activities, e.g., seed quality checks. Figure 3 shows a hierarchy of different checks. A process check would comprise document and numeric checks, which have to be in the correct order with respect to time. Document checks can be compared to list checks, milestone checks, and binary checks based on writing documents (digital or scanned from paper) as well as document completeness. Numeric checks are composed of milestone checks and binary checks as well as basic arithmetic and logical checks. In agriculture, effective compliance is crucial to ensure adherence to regulations, standards, and best practices. A robust compliance framework should incorporate a multi-faceted approach, encompassing process checks, document and numeric checks, and milestone and list checks. Process checks verify the sequence and timing of agricultural processes, such as planting, harvesting, and pesticide application, and they identify any gaps or missing data. Document and numeric checks assess the accuracy, completeness, and format of documents, such as farm records, certification documents, and regulatory reports.