AI Articles and Analysis about Business intelligence and analytics

When it comes to the possibilities for AI in the insurance space, it may seem like machine vision is an inevitable choice for any insurer looking to have as much information on the insured property, car, or people as possible. However, this may still yet to be the case for many insurers. The insurance space has a relative lack of case studies for this type of AI software. This may indicate a lack of demand from the insurance industry or a lack of traction on the part of the technology's use in insurance.

When it comes to the healthcare industry, one might be able to think of numerous use cases for AI approaches like machine vision or predictive analytics. However, the applications of natural language processing (NLP) in healthcare are just as varied.

Episode Summary: One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy.
This week we interview Professor Joan Peckham at the University of Rhode Island. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI.

Banks and investment organizations usually have large research teams that are tasked with investigating and monitoring events that might affect financial trading markets. Investment research is a business function in these firms and is a fundamental part of what is required from analysts, equity managers, investors, and traders.

The insurance industry is making use of various artificial intelligence applications to solve business problems, but perhaps the most versatile is predictive analytics. The ability to aggregate data from disparate sources for business intelligence allows business leaders in insurance to inform important decisions across departments.

The Patient Protection and Affordable Care Act (ACA) has significantly changed the landscape for how healthcare providers can be reimbursed by insurers. They are now reimbursed based on the guidelines set by Value-based Purchasing (VBP) models. This type of payment model reimburses the healthcare provider based on the quality of their work with the insured patient.

The healthcare industry is perhaps second only to finance when it comes to the sheer amount of historical data available for use with artificial intelligence. Data from EMRs, insurance claims, clinical trials, and drug research and development can all be pulled into a machine learning algorithm to generate insights on patient behavior, patient risk, and effective treatments for a variety of conditions, among a variety of others.

Decision-makers in the banking sector have a unique set of business intelligence needs, and artificial intelligence has been on the radar of banking executives for several years now. It follows that AI and machine learning would find their way into business intelligence applications for the banking sector.