Cloud-Native Data Engineering for Regulatory Horizon Scanning: Automating Policy and Compliance Monitoring

Abstract

A cloud native Regulatory Horizon Scanning (RHS) platform is proposed to source and scaling at scalability or malleability to micro service the policy text by the natural language processing (NLP)-based machine learning was also proposed in this article. This will cover over usage of ElasticSearch which aims at indexing and storing parsing equipment and dispersed services (imposed on long term import of new regulations changes). In pilot trial studies within the banking industry, financial services and health it is revealed that the new system has the ability to reduce the average number of days to detect new regulations (12 days on average) by less than 24 hours without loss in the determination even in all directions (more than 90 percent on average). More than 65-percent was taken off the manual examination task and the theoretical duration of the pipeline needed to execute enforcement of the administration in computerized pipelines was close to 70-points shorter. The explicable artificial intelligence was also used to rank the urgent updates on a real-time basis as per the risk rating framework. The HIPAA, PGD and cross-border data regulations were reached as well. These findings suggest that scalable and inexpensive regulatory overseeing is implementable because of an opinion of cloud-native data engineering. The model is an effective case study guidance regular which financiers must monitor to increase their readiness and usefulness to impound AI-oil-subsisted analytics and accompanying management into a commendable stage.