In a closed-door simulation, insurers played out their response to a mass disruption by China's Volt Typhoon hackers--and found a nightmare scenario. It's around an hour and 10 minutes into the role-playing game I've been invited to observe, a simulated catastrophic cyberattack on US water utilities, when the whole thing begins to feel less like a fun afternoon playing Dungeons & Dragons and more like a plausible threat to civilization. A full 24 hours of in-game time have passed since hackers disrupted 5,000 water utilities across the United States in this imagined scenario. Joshua Corman, the former Cybersecurity and Infrastructure Security Agency strategist serving as our dungeon master, stands at the front of a conference space in an office tower high above Times Square, narrating the latest updates to the game's participants, a few dozen insurance executives set up in six teams. All of them have gone disturbingly silent. It's about to get harder," Corman says. "I'm going to share a few things, and it's going to hurt." It is, of course, still the same April afternoon as when we started--but in game time, the second-order effects of widespread water outages have started to become clear. Food refrigeration systems are failing at cold storage warehouses. Water-dependent drug and chemical manufacturing has been bottlenecked, leading to insulin shortages. Data centers' cooling systems are failing, causing outages of cloud services. Most critically, 2,000 hospitals are without water, hampering patient care and in some cases leading to evacuations as HVAC systems shut down and the July heat--the game takes place just before Independence Day in 2027--bakes facilities. Worse yet, Corman is playing a looping video onscreen, at the front of the room, showing a burst water main: The hackers have managed to trigger not just IT disruption but also, in at least some cases, real physical destruction that will take far longer to fix. "Everyone downstream is without water pressure," Corman says. "There are no breaks in real incident response," Corman explains just before the giant water pipe starts gushing onscreen. "If you have to go to the bathroom, go to the bathroom.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Is RFK Jr. better on women's health than Newsom? We're about to find out Halle Berry speaks Wednesday during the New York Times DealBook Summit, where she criticized Gov. Gavin Newsom for vetoing a bill that would guarantee access to menopause treatment for California women. This is read by an automated voice. Please report any issues or inconsistencies here .

This paper describes the experiences and lessons learned after the deployment of a country-scale environmental digital twin on the island of Cyprus for three years. This digital twin, called GAEA, contains 27 environmental geospatial services and is suitable for urban planners, policymakers, farmers, property owners, real-estate and forestry professionals, as well as insurance companies and banks that have properties in their portfolio. This paper demonstrates the power, potential, current and future challenges of geospatial analytics and environmental digital twins on a large scale.

Firstly, we thank the reviewers for their valuable comments. Whilst it is not reasonable in practice to assume that data is sampled i.i.d. As previously stated, we believe our work forms a first step in achieving this goal. We believe that our theoretical model captures this dynamic. An insurance company may gather information from a customer to better evaluate potential risk.

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains -- automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels -- (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Chris Heinrich will never forget the winter day he and his family evacuated their home in Altadena, California, as a vertical wall of flame was slowly bearing down on their neighborhood from the mountains. "It was dark," he told Slate. "There was no internet, my daughter was crying, the wind was blowing." Even as the fires approached, he said, he didn't really believe that their house would burn.

The extraction of information about traffic accidents from legal documents is crucial for quantifying insurance company costs. Extracting entities such as percentages of physical and/or psychological disability and the involved compensation amounts is a challenging process, even for experts, due to the subtle arguments and reasoning in the court decision. A two-step procedure is proposed: first, segmenting the document identifying the most relevant segments, and then extracting the entities. For text segmentation, two methodologies are compared: a classic method based on regular expressions and a second approach that divides the document into blocks of n-tokens, which are then vectorized using multilingual models for semantic searches (text-embedding-ada-002/MiniLM-L12-v2 ). Subsequently, large language models (LLaMA-2 7b, 70b, LLaMA-3 8b, and GPT-4 Turbo) are applied with prompting to the selected segments for entity extraction. For the LLaMA models, fine-tuning is performed using LoRA. LLaMA-2 7b, even with zero temperature, shows a significant number of hallucinations in extractions which are an important contention point for named entity extraction. This work shows that these hallucinations are substantially reduced after finetuning the model. The performance of the methodology based on segment vectorization and subsequent use of LLMs significantly surpasses the classic method which achieves an accuracy of 39.5%. Among open-source models, LLaMA-2 70B with finetuning achieves the highest accuracy 79.4%, surpassing its base version 61.7%. Notably, the base LLaMA-3 8B model already performs comparably to the finetuned LLaMA-2 70B model, achieving 76.6%, highlighting the rapid progress in model development. Meanwhile, GPT-4 Turbo achieves the highest accuracy at 86.1%.

Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional requirements increases. These requirements include security, safety, privacy, and, increasingly, non-discrimination. Motivation. Fairness in pricing algorithms grants equitable access to basic services without discriminating on the basis of protected attributes. Method. We replicate a previous empirical study that used black box testing to audit pricing algorithms used by Italian car insurance companies, accessible through a popular online system. With respect to the previous study, we enlarged the number of tests and the number of demographic variables under analysis. Results. Our work confirms and extends previous findings, highlighting the problematic permanence of discrimination across time: demographic variables significantly impact pricing to this day, with birthplace remaining the main discriminatory factor against individuals not born in Italian cities. We also found that driver profiles can determine the number of quotes available to the user, denying equal opportunities to all. Conclusion. The study underscores the importance of testing for non-discrimination in software systems that affect people's everyday lives. Performing algorithmic audits over time makes it possible to evaluate the evolution of such algorithms. It also demonstrates the role that empirical software engineering can play in making software systems more accountable.

The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance industry remains underexplored. The complexity of insurance knowledge, encompassing specialized terminology and diverse data types, poses significant challenges for both models and users. To address this, we introduce InsQABench, a benchmark dataset for the Chinese insurance sector, structured into three categories: Insurance Commonsense Knowledge, Insurance Structured Database, and Insurance Unstructured Documents, reflecting real-world insurance question-answering tasks.We also propose two methods, SQL-ReAct and RAG-ReAct, to tackle challenges in structured and unstructured data tasks. Evaluations show that while LLMs struggle with domain-specific terminology and nuanced clause texts, fine-tuning on InsQABench significantly improves performance. Our benchmark establishes a solid foundation for advancing LLM applications in the insurance domain, with data and code available at InsQABench.