Harnessing Big Data and AI in Cloud-Powered Financial Decision-Making for Automotive and Healthcare Industries: A Comparative Analysis of Risk Management and Profit Optimization

Abstract

This study compares risk management and profit optimisation in cloud-powered financial decision-making for the automotive and healthcare industries using big data and artificial intelligence. The analysis of healthcare data is expected to inform future public healthcare policy development. In order to make fact-based and accurate healthcare policy decisions, this research analyses whether big data analytics could be systematically integrated into the health policy cycle. This research investigates BDA's potential for accurate and fast healthcare policymaking. PRISMA was used to construct a conceptual framework. BDA in health care policy is introduced, its benefits discussed, a framework presented, examples from the literature introduced, obstacles identified, and suggestions provided. BDA may turn traditional policy-making into data-driven, correct health policy decisions, according to this research. According to this research, BDA may be used in evaluation of health policies, policy identification, development, implementation, and agenda setting. Today, public health policy choices are based on descriptive, predictive, and prescriptive analytics from electronic health reports, public records of health, clinician and patient information, & government as well as social net sites. To use all the information, one must overcome computational, algorithmic, technical, legal, normative, governance, and policy constraints in today's increasingly diverse data world. To maximise its value, big data must be shared. It allows public health organisations and policymakers to assess population-level policy impacts and risks.