Machine Learning Applications for Enhancing Regulatory Compliance in Blockchain-Based Supply Chains
Abdul Subhani Shaik, M. Mahima, Jajimoggala Sravanthi, Hassan Ali, Rohit Agarwal, Dilli Ganesh
Abstract
Therefore, there is a need to formulate a predictive analytics approach to get increased regulatory compliance within blockchain supply chain management. The plan is to apply machine learning to determine probabilistic outcomes that can be used in attempting to identify future compliance concerns and trends derived from analysis of past blockchain transactions. The model also becomes aware of the patterns in the supply chain data and hence estimates regulatory changes before the noncompliance incidents happen. Besides, smart contracts that include machine learning aspects allow for the automatic execution of regulation adherence. The system includes other document check mechanisms which utilize a Technical feature of artificial intelligence to ascertain the validity of documentation in the supply chain. Furthermore, risk assessment algorithm fosters compliance regulation attention by highlighting key compliance risks within the supply chain. By so doing, the system has the capability to identify fraudulent transactions and policies in an effort to prevent them as well as meet set compliance levels. In conclusion, this forward-looking work provides a holistic framework on how predictive analytics can aid in the kind of decision-making required to strengthen the transformative power of blockchain supply chains in the areas of compliance, openness, and responsibility.