Finding suitable health insurance coverage can be challenging for individuals and small enterprises in the USA. The Health Insurance Exchange Public Use Files (Exchange PUFs) dataset provided by CMS offers valuable information on health and dental policies [1]. In this paper, we leverage machine learning algorithms to predict if a health insurance plan covers routine dental services for adults. By analyzing plan type, region, deductibles, out-of-pocket maximums, and copayments, we employ Logistic Regression, Decision Tree, Random Forest, Gradient Boost, Factorization Model and Support Vector Machine algorithms. Our goal is to provide a clinical strategy for individuals and families to select the most suitable insurance plan based on income and expenses.

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.

Ensuring financial protection and access to needed healthcare is integral to achieving Universal Health coverage (UHC) which is integral to the achievement of Sustainable Development Goal (SDG) 3. The uptake of health insurance has been poor in Nigeria, and this has been due to a lot of challenges which include access to healthcare facilities, beliefs, low level of awareness about health insurance, policy challenges, poverty, and where to get required information (2-4). A significant step to improving this includes improved awareness, access to information and tools to support decision making (5). Recommender systems are designed to assist individuals to deal with a vast array of choices, it takes advantage of several sources of information to predict options and preferences around specific items (6-8). Recommender systems enhance the user experience by giving fast and coherent suggestions. Artificial intelligence (AI) based recommender systems have gained popularity in helping individuals find movies, books, music and different types of products on the internet including diverse applications in healthcare (9-12). It has also been used in the insurance industry to support decision making on insurance products (13). Recommender systems are in three main categories which include: collaborative filtering, content-based and hybrid filtering (9). Collaborative filtering method uses the data from other users rating of items to make recommendation for a user for those items.

Regardless of where you live or who you are, protecting your home, assets and loved ones will always be a key concern. In a world full of uncertainty, people from all backgrounds want to be sure they're safeguarded from threats, potential disasters and loss of property. Enrolling in home, auto, health or other types of insurance can bring peace of mind, but as anyone who has tried to navigate the buying exchanges will know, this can often be easier said than done. The barriers to enrollment are many, and with so many complicated coverage options, convoluted eligibility requirements and fine print to sort through, the insurance industry is in need of a makeover. Thankfully, the insurtech industry has arisen to do just that.

Artificial intelligence (AI) has been the promise of healthcare for nearly a decade, but the industry has yet to adopt it widely. Applications of AI in arguably more difficult domains, such as search, language and image recognition, have seen massive success over the past decade. While neural net algorithms and compute power have improved dramatically, AI in healthcare is still lagging behind. The big reason these domains, and not healthcare, have been able to utilize AI tech is due to the internet's ability to make massive amounts of data available. Now data access via internet technologies is finally happening in healthcare through secure channels.

Artificial intelligence has become an integral part of many technology applications and software, from what online ads shoppers see to how spam gets filtered out of our email inboxes. Investors who are looking for technology companies that are already succeeding in using AI to accelerate their businesses should consider buying CrowdStrike Holdings (NASDAQ:CRWD), Lemonade (NYSE:LMND), and Amazon (NASDAQ:AMZN). Read on to find out why these companies might be worth your investment right now. CrowdStrike is a fast-growing cloud-based cybersecurity company that has woven AI into its applications. The company's Falcon endpoint security platform is powered by AI that runs on the company's proprietary Threat Graph database.

Health insurance is a critical component of the healthcare industry with private health insurance expenditures alone estimated at $1.1 billion in 2016, according to the latest data available from the Centers for Medicare and Medicaid Services. This figure represents 34 percent of the 2016 National Health Expenditure at $3.3 trillion. In this article, we will look at four AI applications that are tackling problems of underutilization and fraud in the insurance industry. Some applications below claim that they are using artificial intelligence to help improve health insurance cost efficiency, while reducing waste of money on underutilized or preventable care. Other applications claim to detect fraudulent claims.

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.

Artificial Intelligence is probably a term so widely used these days that almost everyone is familiar with the concept, if not an expert on what it means. This technology is making waves in every industry. This includes the insurance industry. Artificial Intelligence is a technology that enables computers to perform the same communication tasks that a human would have otherwise. Apart from communicating, computers can also perform other tasks.

The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist the AI expansion; the fear of unemployment and the lack of trust in cognitive systems are among them. But these are hardly justified concerns.