This paper proposes a reinforcement learning (RL) framework for insurance reserving that integrates tail-risk sensitivity, macroeconomic regime modeling, and regulatory compliance. The reserving problem is formulated as a finite-horizon Markov Decision Process (MDP), in which reserve adjustments are optimized using Proximal Policy Optimization (PPO) subject to Conditional Value-at-Risk (CVaR) constraints. To enhance policy robustness across varying economic conditions, the agent is trained using a regime-aware curriculum that progressively increases volatility exposure. The reward structure penalizes reserve shortfall, capital inefficiency, and solvency floor violations, with design elements informed by Solvency II and Own Risk and Solvency Assessment (ORSA) frameworks. Empirical evaluations on two industry datasets--Workers' Compensation, and Other Liability--demonstrate that the RL-CVaR agent achieves superior performance relative to classical reserving methods across multiple criteria, including tail-risk control (CVaR$_{0.95}$), capital efficiency, and regulatory violation rate. The framework also accommodates fixed-shock stress testing and regime-stratified analysis, providing a principled and extensible approach to reserving under uncertainty.

More than 12,000 people are turning 65 each day in the US. And with that, individuals and families are starting to make considerations on what might be entailed to better manage the aging process. There is a strong desire from seniors to age in place, meaning staying in their home instead of moving to a dedicated facility. Marc Glickman, CEO of long-term care planning experts BuddyIns, estimated that today, around 75% of seniors are using home care services to age in place instead of moving to an assisted living or nursing homes. An AARP survey showed 90% of individuals 65 and over would prefer to age in place.

Offering advanced technology content on vehicles often results in a steep increase in problems experienced, according to the J.D. Power 2022 U.S. Tech Experience Index (TXI) Study, released . Vehicle quality, as expressed in problems per 100 vehicles (PP100), is a common measure within both the TXI Study, focused on advanced vehicle technology as it first comes to market, and the annual J.D. Power Initial Quality StudySM (IQS). Of the advanced technologies included in the 2022 TXI Study, 46% of them had at least one problem with a PP100 higher than the most problematic attribute included in the 2022 IQS, with some exceeding it several times over. A low PP100 score indicates better quality. AF Group, a nationally recognized holding company whose affiliated brands provide specialty and workers' compensation insurance solutions across the United States, and CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology for commercial insurance claims optimization, announced a new initiative to deploy AI technology to improve medical outcomes for injured workers, while also reducing claims losses for the company's workers' compensation policyholders and streamlining claims management.

AF Group companies will use CLARA's industry-leading technology platform to identify the health care providers best suited to help injured employees recover successfully and return to work quickly. AF Group comprises seven affiliated insurance brands that provide innovative, specialty insurance solutions. AF Group companies utilize industry-leading best practices, analytics and resources to help manage risk and minimize losses for policyholders -- and always strive to provide injured workers with security, compassionate care, and the opportunity to return to work as soon as possible. "Innovation in data analytics is a key pillar in our strategy to be the best at what we do, maintaining a culture of claims excellence and compassionate care for our customers," said Paul Kearney, Chief Claims Officer at AF Group. "Returning to work after an injury helps employees rebuild their livelihoods and restore their quality of life. AF Group companies help those workers using a multifaceted approach that incorporates data analytics, evidence-based medicine, and smart technology. CLARA Analytics aligns nicely with that strategy -- helping injured employees recover quickly while also minimizing losses for policyholders and improving our claims management processes."

CCC Information Services Inc. announced it is updating its name to CCC Intelligent Solutions Inc. The new name reflects the company's focus on applying AI, IoT, and advanced analytics to power mission-critical workflows, commerce and connections across the multi-trillion-dollar insurance economy. Sapiens International Corporation announced the roll-out of its newly upgraded CoreSuite for Workers' Compensation software package with the Ohio Bureau of Workers' Compensation, which is the first insurance carrier to complete this upgrade. The upgrade is designed to better optimize operational efficiencies, increase customer satisfaction and increase profitability for workers' compensation providers, administrators and state funds.

Azure's machine learning capabilities enhanced Clover's company-wide digital transformation initiative and provided the company with data that uncovered major inefficiencies in inventory, transportation and distribution sectors. For example, at the company's distribution centers, Azure's machine learning and data analysis capabilities help minimize order assembly by determining what products and how much to stock in each warehouse and where best to stock products within the warehouse. Thanks to the technology, processing teams nearly tripled their efficiency.

A job description with "acquire AI superpowers" might appeal to millennials more than "study policy footnotes and calculate claim reserves." Commercial insurance faces a growing claims adjuster staffing gap. On the retirement end, there's a rising tide of experienced adjusters leaving the profession. According to the Pew Research Center, nearly 10,000 baby boomers retire each day in the U.S., and about 25% of them leave positions in the insurance and financial services sector. Seasoned adjusters leave with a wealth of experience built up over decades, leaving newer adjusters to handle a rising volume of claims.

AI alerts have played and continue to play a critical role in detecting and controlling future outbreaks like COVID-19. Did you know that artificial intelligence (AI) technology first sounded the alarm on COVID-19? An algorithm developed by BlueDot, a Canadian AI firm, scoured news reports and airline ticketing data to detect the outbreak on Dec. 31, 2019 in China. On the same day, HealthMap, a Boston Children's Hospital website using AI, spotted a news report of a new type of pneumonia in Wuhan, China, and alerted global health officials. HealthMap was also the first to notify Chinese health officials that COVID-19 was expanding outside of China.

Artificial intelligence or AI stands out as one of the hottest technologies in the insurance industry in 2018. We are seeing more insurers identifying use cases, partnering, and investing in AI. 85% of insurers are investing time, money, and effort into exploring the AI family of technologies. The focus is not so much on the technology itself as on the business challenges AI is addressing. Through our annual SMA Innovation in Action Awards program, we hear many success stories from insurers throughout the industry that are innovating for advantage. AI was a key technology among this year's submissions.

Attorney involvement has been steadily driving up workers' compensation claims costs over the past decade, and research reveals the problem has never been worse. For example, in 2014, the California Workers' Compensation Institute (CWCI) published results of a five-year study that showed when a single injured party brought in a lawyer, the associated costs per claim went up by an average of $40,000 for permanent disability payments and $25,000 for temporary total disability benefits -- even if the case never went to court. In Florida from 2016-2017, legal fees related to workers' comp claims totaled nearly $440 million -- approximately $254 million of that came from employers defending claims while injured workers themselves were responsible for $186 million (an increase of 36 percent in just one year). And then there is the national scale. In a newly released study from CLARA analytics, data collected from a single national payer from injury years 2007-2017 was focused on closed indemnity claims across multiple states.