AI Articles and Analysis about Business intelligence and analytics

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.

Business leaders in pharma and life sciences have more specific business intelligence needs than peers in other industries. In addition to common business drivers like marketing, profit and loss, and customer churn, pharmaceutical companies need information regarding patients and clinical trial results.

McKinsey estimated that embarking on digital transformation to restructure value chains and drive R&D innovation across the pharmaceutical industry could be worth $50–150 billion of earnings before interest, taxes, depreciation, and amortization. In particular, machine learning is likely to continue finding a place in the pharmaceutical industry. Pharmaceutical companies have found applications for machine learning ranging from drug discovery to clinical trial retention.
The State of AI in the Asian Pharmaceutical Industry
AI seems to be making its way into the pharmaceutical space in Asia over the last two or three years, particularly in China and Japan. For the most part, the companies offering or using AI for drug discovery are just starting to acquire funding and talent. XtalPi seems to have the highest density of talent with a decent likelihood of being able to work with machine learning.

There are few companies claiming to offer artificial intelligence solutions to orthopedics companies. We found that these solutions are intended to help orthopedics companies with at least one of the following business problems: