The insurance company uses different factors to classify the policyholders. In this study, we apply several machine learning models such as nearest neighbour and logistic regression to the Actuarial Challenge dataset used by Qazvini (2019) to classify liability insurance policies into two groups: 1 - policies with claims and 2 - policies without claims. The applications of Machine Learning (ML) models and Artificial Intelligence (AI) in areas such as medical diagnosis, economics, banking, fraud detection, agriculture, etc, have been known for quite a number of years. ML models have changed these industries remarkably. However, despite their high predictive power and their capability to identify nonlinear transformations and interactions between variables, they are slowly being introduced into the insurance industry and actuarial fields.

Artificial Intelligence (AI) has received an increasing amount of attention in multiple areas. The uncertainties and risks in AI-powered systems have created reluctance in their wild adoption. As an economic solution to compensate for potential damages, AI liability insurance is a promising market to enhance the integration of AI into daily life. In this work, we use an AI-powered E-diagnosis system as an example to study AI liability insurance. We provide a quantitative risk assessment model with evidence-based numerical analysis. We discuss the insurability criteria for AI technologies and suggest necessary adjustments to accommodate the features of AI products. We show that AI liability insurance can act as a regulatory mechanism to incentivize compliant behaviors and serve as a certificate of high-quality AI systems. Furthermore, we suggest premium adjustment to reflect the dynamic evolution of the inherent uncertainty in AI. Moral hazard problems are discussed and suggestions for AI liability insurance are provided.

Today, major healthcare companies are investing heavily into various AI-powered devices. For example, Zimmer Biomet and the New York City-based Hospital for Special Surgery recently inked a three-year deal to create the HSS/Zimmer Biomet Innovation Center for Artificial Intelligence in Robotic Joint Replacement. "The collaboration aims to develop decision support tools--powered by data collection and machine learning -- to assist surgeons planning and predicting outcomes for robotic-assisted joint replacements." Additionally, Johnson & Johnson have gone on record saying that they see "a huge opportunity to harness data, machine learning and artificial intelligence to help drive decision-making at all levels of healthcare." As artificial intelligence starts playing a larger role in the modern healthcare space, a critical question will need to be answered: Are AI-powered solutions products or services?

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care" explores how AI liability insurance can mitigate predictable risks and uncertainties to health care AI adoption. The big challenge for health care delivery is overcoming institutional mismatch, according to Stern. "The technologies that have the greatest potential to transform health care delivery --this includes, but is not limited, to AI -- would be unrecognizable to the 20th-century architects of our regulatory and health care delivery institutions," says Stern. "And this problem is getting worse. The pace of innovation that we see today coupled with our rapidly transforming analytical and technological capabilities is increasingly mismatched to our existing institutions."

Alex Khizhniak, director of Technical Evangelism at IT services provider Altros, stated, "Being connected to other cars on the road will eventually make driving much safer. Combined with predictive analysis, smart systems could substitute for a driver in case of emergency. Although these technologies are still developing - and some legislations should also be introduced- the future looks promising for self-driving and intelligent driving assistants." While many have been vocal about their concerns regarding the regulation of autonomous or connected cars, there are many advantages that must be considered before delving into the risks. One of the many key benefits of connected cars is that they could contribute to safer traffic patterns in cities with congestion issues as a consequence of rapid urbanization.

Small businesses are generally unprepared for unforeseen catastrophes, surveys show. A whopping three-fourths of U.S.-based outfits say they don't have an insurance policy that meets their unique needs, while 40% admit they don't have coverage of any kind. Of course, the latter are on the hook for incidents like client complaints, contract disputes, and employee injuries, in addition to burglary or theft and customer injury -- all of which can amount to hundreds of millions of dollars in repairs and remedial disbursements. That's why in 2017 former McKinsey business analyst and Morgan Stanley associate Bryan O'Connell founded Huckleberry, a carrier built on a robust cloud-based software and data science and analytics backend. The San Francisco-based company ambitiously aims to digitize the purchase and management of commercial insurance, a category of coverage that's notoriously slow to acquire and which historically has been wrapped up in layers of bureaucracy.