## 7 RegTech and Industrial Internet of Things
### Potential IIoT-Based Regulatory Supervision Framework  
The presence of supervisory activity is a sign of the regulator's distrust of the regulated entities. Traditionally, the supervisors may need to audit producers on site to confirm their actions comply with the regulatory requirements. But it is more difficult to ascertain compliance immediately after such spot checks. Although many of the producers follow the rules, some of them can violate them to reduce costs or pursue higher profits. One possible solution is real-time supervision. There are two ways to conduct it. One is that the producers can proactively report to regulators or supervisors. The other way is that regulators can passively acquire the reports. From the regulators' perspective, the former way has prominent pros such as lower cost and high efficiency, but the data might be tampered with as they are second-hand sources. The disadvantages of the latter approach are higher costs and inefficiency. However, the data are authentic (see Figure 5). Regulators want authentic data, while supervision needs to be cost-effective and efficient. To achieve this, the ideal solution would be to map real-world production processes onto the web applications with technologies such as _digital twins_[110]. This would allow for real-time monitoring and management, which could become as easy as supervision in the financial sector. However, it is difficult to achieve this in all industries worldwide at the moment. Nevertheless, there are a few options that can be tried to become closer to this goal:  
* _IoT-based Data Collection_: this not only saves labor costs for producers for data collection by replacing traditional manual work but also increases the transparency of the process.
* _Necessary Data Verification_: this measure prevents data fraud risks such as IoT devices being damaged or tampered with by humans. It can be achieved by introducing AI and ML technologies to assist decision-making after certain algorithms are trained [111].
* _On-site Audit_: Activities in the non-financial sectors involve fieldwork. A proper on-site audit is necessary, as supervisors can only partially rely on online supervision. For example, pesticide spraying is important for the cultivation of crops because it is effective against pests and diseases. However, excessive pesticide use is a serious threat to human health, and therefore precise control of pesticide doses is very important. It is therefore important to check records of pesticide purchases and actual use.
* _Real-Time Supervision_: Real-time monitoring in some critical procedures with automatic reporting to regulators can help manufacturers comply with regulations with less labor cost in data collection as well as establish a good reputation. Machine learning can help companies eliminate potential risks by using daily data to predict their occurrence in advance. On-premises real-time monitoring is difficult, as companies would be reluctant to set up such infrastructure without any benefit. However, manufacturers can also benefit if the IIoT is deployed for both their own production and compliance monitoring. While these tools can solve some problems, they can also create new ones. For example, the daily collected data are of high volume, and how to safely process, share, and store it is a significant issue. The algorithms training for data verification takes time and money before they can make precise judgments. The frequent on-site audit is expensive for supervisors and a burden for producers. The regulator needs to balance the frequency and cost of data verification and its negative impact on industrial innovation. Real-time supervision may result in data privacy issues.