Luigi Mangione, the man suspected of killing the chief executive of UnitedHealthcare, underwent surgery and was reported missing in San Francisco before the shooting. Brian Thompson, 50, CEO of the healthcare insurance giant, was gunned down last week in Midtown Manhattan, spawning a five-day manhunt that eventually led to Mangione's arrest at a McDonald's restaurant in Altoona, Pa. Questions about Mangione's alleged motives and background have swirled in the media since his arrest Monday. As prosecutors worked to bring him to New York to face charges, new details emerged about his life and his capture. The 26-year-old Ivy League graduate from a prominent Maryland real estate family was charged with murder hours after his arrest.

The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insurance industry datasets without requiring the datasets themselves to be shared from one company to another. The application of FL addresses two of the most pressing concerns: limited data volume and data variety, which are caused by privacy concerns, the rarity of claim events, the lack of informative rating factors, etc.. During each round of FL, collaborators compute improvements on the model using their local private data, and these insights are combined to update a global model. Such aggregation of insights allows for an increase to the effectiveness in forecasting claims losses compared to models individually trained at each collaborator. Critically, this approach enables machine learning collaboration without the need for raw data to leave the compute infrastructure of each respective data owner. Additionally, the open-source framework, OpenFL, that is used in our experiments is designed so that it can be run using confidential computing as well as with additional algorithmic protections against leakage of information via the shared model updates. In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions. This paper's application of FL can also be expanded to other areas including fraud detection, catastrophe modeling, etc., that have a similar need to incorporate data privacy into machine learning collaborations. Our framework and empirical results provide a foundation for future collaborations among insurers, regulators, academic researchers, and InsurTech experts.

Nobody goes to the circus to see the net. But when the high-wire gymnast slips, the net is suddenly the star of the show. So, don't think of me as being in the health insurance industry, the safety net of your life. I don't want you to stop reading. I'll tell you I'm an expert in customer service and have been persistently challenged by innovators in the retail and tech space who have conditioned the consumer to expect instant responses, instant results and instant products.

The insurance industry has undergone significant changes over the years. The integration of advanced technologies such as artificial intelligence (AI) has paved the way for further evolution, offering improved efficiency, reduced costs and enhanced customer experience. Various AI applications are currently in use in the insurance industry, ranging from underwriting to claims processing. AI can help insurers evaluate risk more accurately by analyzing large amounts of data such as historical claims data, credit scores and social media activity--thereby enabling insurers to offer personalized coverage to customers and price policies more accurately. It can also aid in detecting and preventing fraud by analyzing data patterns and identifying suspicious activity, which can help insurers save money by reducing the number of fraudulent claims they pay out.

As the Insurtech industry continues to evolve rapidly, it's essential to stay informed about the latest trends shaping the sector. To help you stay ahead of the curve, we've gathered insights from 14 industry experts on the most significant trends happening in Insurtech today. From personalized insurance offerings to the adoption of AI and blockchain technology, these trends are revolutionizing the way insurance is delivered and experienced by customers. A growing trend within Insurtech is the development of personalized insurance offerings. By utilizing AI and advanced analytics to analyze customer data, Insurtech companies can provide tailored insurance products based on individual needs and risk profiles.

One of the most significant changes in recent years in the insurance sector has been the incorporation of artificial intelligence (AI) into various phases of the insurance process. From underwriting to claims processing, artificial intelligence has the potential to transform the business by increasing efficiency, lowering costs, and improving customer experience. In this article, we will look at the function of artificial intelligence in insurance and its possible impact on the sector. Underwriting is an important part of the insurance process that involves assessing potential policyholders' risks and establishing the appropriate premium. This has traditionally been a time-consuming and labor-intensive procedure, but artificial intelligence has the potential to make it faster, more efficient, and more accurate.

These days, artificial intelligence seems to be everywhere. You can check the weather just by asking for the temperature out loud, and it can tell you when it's time to order more laundry detergent before you've even added it to the grocery list. But other areas that aren't always top of mind, like insurance, have also begun to use artificial intelligence. AI has made a lot of positive changes throughout the insurance industry. For example, by tracking policy-holders' behavior behind the wheel, auto insurance companies have been able to encourage more responsible driving and try and help reduce risky behavior.

Breaking news at the time of writing is that American artificial intelligence (AI) company OpenAI has released Generative Pre-trained Transformer 4 – more commonly known as GPT-4 (14 March 2023). The launch of this latest multimodal large language tool further increases the AI opportunities and risks facing the insurance industry. This latest version of OpenAI's chatbot can respond to images and it processes around eight times as many words as the original ChatGPT model launched in November 2022. Trained on text taken from the internet, ChatGPT has been designed to provide quick and understandable answers to any question. Read: AI has'enormous potential benefits' for insurance but regulators should target'safe and responsible adoption' – Kennedys Ian McKenna, chief executive of the Financial Technology Research Centre, said: "If you look at what some of these chatbots can do now and extrapolate what they will be able to do in four or five years' time, it's really quite scary. "People won't have to remember facts and data in the same way and it will have an enormous impact on insurance on so many fronts.

The upstart ChatGPT heralded an advent of conversational-AI platforms that can passably converse with humans based on a wide range of inputs. In addition to ChatGPT, which is made by the Microsoft-backed nonprofit OpenAI, other big tech companies are getting into the game with competing projects from Google (Bard) and Facebook (LLaMA). The rise of AI to kitchen-table prominence raises a question: Are insurance companies, which have been transforming digitally for years, ready to invest further in large language models and turn their precious customer relationships over to a chatbot? Forrester Principal Analyst Indranil Bandyopadhyay says that a big AI revolution in insurance isn't going to happen overnight. "I don't see the majority of the insurance industry going and jumping into these emerging technologies. It will take some time," Bandyopadhyay says.

Like most sectors that have seen consumer adoption of digital technologies accelerate since the pandemic, the insurance industry is undergoing a major transformation, with new technologies and business models making it possible for insurers to offer highly flexible and personalized coverage. For an industry that has historically moved slow in adopting technology, 2023 promises to be a challenging year for insurers – but one that will make a tremendous impact on their relationship with customers. In the next decade, the insurance industry as we know it will be unrecognizable. Cars, homes, and individuals will all be insured within highly flexible insurance programs as a matter of course. These programs will include sophisticated mechanisms to dynamically and automatically adjust coverage, ensuring that it is optimal and personalized at any given moment.