保险精算人每日学习平台简报

日期：2026-07-15

今日总览

今日主题学习任务

主题：周三：财险、车险与巨灾风险

学习目标：关注车险定价、巨灾风险、责任险、费用率、赔付率和外部生态数据。

建议产出：

  1. 选择一条财险或车险新闻，找出影响赔付频率或案均赔款的变量。
  2. 写出一个巨灾或新能源车险的风险分层思路。
  3. 记录一个可用于定价或反欺诈的数据源。

平台板块索引

每日精算概念复习

赔付率 Loss Ratio：赔款及相关理赔成本相对保费的比例，是健康险、财险和车险经验分析核心指标。

精算人练习：把赔付率拆成出险频率、案均赔款、责任结构和渠道质量四个驱动。

1. Insurance Doesn’t Have an AI Problem...

摘要：Insurance Doesn’t Have an AI Problem... Customer Experience ITL Editorial Team Wed, 07/08/2026 - 22:32 ...It has a design problem. Insurers risk wasting AI investments by prioritizing technology over understanding the workflows and needs of the people using it. July 9, 2026 Image Every AI conversation in insurance right now seems to start in the same place. Models, platforms, copilots, automation. So it might sound strange to say the industry's AI problem isn't a technology problem. It's a design problem. I'm the founder and CEO of a design agency that has worked with insurance companies for more than 15 years, and I keep seeing the same pattern. We invest in technology before we understand the people who are supposed to use it. Then, when adoption is low, we blame it on people. AI is just the latest, highest-stakes version of that same old mistake. Whether investments in AI pay off won't come down to what model you pick. It'll come down to whether the people you built it for actually use it. I am a designer by trade: art school, design school, maker through and through. Cake & Arrow did not start in insurance. We began in retail and e-commerce, designing digital experiences for consumer-centric brands. Then, about 15 years ago, a CIO at an insurance company asked us to help reimagine a sales platform for agents. We came in as outsiders, and that outside view helped us see something people deep inside the business often can't: insurance experiences are too often built around the business, the policy, and the transactional moments—not around the customers, employees, agents, and brokers trying to navigate them. Investing in Technology Is Not the Same as Progress The instinct in insurance is often to start with the technology. A new tool shows up, a new capability emerges, and every executive team wants to show progress. I get the pressure. Boards are asking about AI. Everyone wants to move fast. But buying technology is not the same as making progress. A powerful AI tool is still worthless if it isn't solving an actual problem for an actual person. The most advanced chatbot, copilot, or automation platform will fail if it gets bolted onto a broken process. That's the actual risk with AI right now. Making the same old mistake, only faster and at greater expense. When talking to insurers, I often make a distinction between "design" and "Design with a capital D." When I talk about "Design," I'm not talking about colors, fonts, or pretty screens. Design is the research, the strategy, and the deliberate decision-making underneath every product and experience. It's understanding who a tool is for, what its purpose is, where the work breaks down, and how a solution earns its place in someone's day. And here's the thing: Design is already happening, whether companies acknowledge it or not. Every agent portal, claims experience, policyholder app, and AI workflow is the result of a decision someone made. The real question is: where and with whom did the decision originate? With the person doing the work, or with an executive mandate, business requirement, vendor pitch, or short-term goal? Too often, the decisions made in insurance have little to do with the human beings they impact. Agents Are Not the Barrier In our recent report, The Connective Thread: From Agent and Broker Research to a New Design Vision for AI-Enabled Insurance Work , we spoke directly with agents and brokers about how they are using AI today, where they are finding value, and what is still getting in the way. What stood out was not the agents' resistance, but their resourcefulness. Agents are already experimenting. They're drafting emails, summarizing policies, comparing quotes, prepping for meetings, and translating complex insurance language into something clients can actually understand. Some are quietly building workarounds because the official systems around them do not support how they actually work. So the problem is not that agents do not want to use AI. It's that the tools too often do not map to the real friction in their work. The industry keeps talking about AI as an automation story. But when you talk to agents, what they want is integration. They are not asking for another tab, login, or disconnected assistant. They're already moving between agency management systems, CRMs, email, spreadsheets, carrier portals, and rating tools. They're entering the same information over and over, hunting across systems for context and trying to track what changed, what a client needs, and what follow-up might fall through the cracks. That is not a single-task productivity problem. It is a workflow problem. AI that helps write an email is useful. AI that understands the context behind the email, pulls from the right systems, shows where the information came from, flags what needs review, and keeps the human in control… that's something else entirely. That is where AI becomes connective tissue, instead of one more tool to add to the pile. Design Around People, Not Around Replacing Them For decades, the insurance industry has strived for ways to disintermediate agents. AI has only added fuel to that fire. There's a real temptation to see AI as a way to replace human labor, cut costs, and eliminate the messiness of human relationships. But that framing misses where the value actually lives. Sure, AI can create efficiencies. It can reduce administrative burden, help agents manage bigger books, and spend less time on repetitive work. But if your starting point is replacement, you'll miss the bigger opportunity to design tools that unlock capacity, judgment, and relationship-building. The best agents are valuable because they know what matters. They understand their clients, and they understand risk. They can feel when something is off, and they know what to ask next. That's how they turn complexity into confidence. AI should be making more room for that work, not pushing it to the side. This is where human-centered design stops being a nice-to-have and becomes a business necessity. If you want agents to adopt AI, you have to understand how they actually work, not how leadership assumes they work. And that requires more than a survey. It means observing real workflows, listening for friction, and noticing the invisible work that quietly holds the system together. Research embedded in the design process points toward solutions. Adoption Is the Whole Game One of the biggest misconceptions about AI is that adoption is what happens after the rollout. It's not. Adoption is the whole game. A tool is only successful if the people it is built for actually want to use it. People want tools that fit into their world, solve problems they recognize, and make their work meaningfully better in a way they can feel. Insurance has a long history of underestimating this. The industry spends significant money on technology that never lands because it has never fully accounted for the human experience surrounding it. Then, when usage is low, the conclusion is often that people are "resistant to change." Most of the time, that's the wrong diagnosis. People are not resistant to change that helps them. They're resistant to tools that make their day harder, add complexity, create risk, ask them to trust outputs they cannot verify, or worse, are designed to replace them. AI cannot simply generate confident answers. It has to earn trust. Agents need to see where information came from, verify recommendations, correct outputs, and approve what goes to a client. "Trust but verify" is not just a user preference here. It's a design requirement. What Leaders Should Do Differently If a carrier, brokerage, or insurtech CEO asked me where to start right now, I'd say this: Catch yourself before you jump to the solution. The pressure to move fast is real, and speed does matter. But moving fast doesn't mean skipping the work that makes speed useful. Before you decide what AI feature to build or what vendor to buy, sit with these three questions: Who is this for? What problem are we solving? And what outcome are we actually after? Then go talk to the people who'll use it. Watch how they work, find where the friction really lives, and let that learning shape the AI strategy before the roadmap hardens. A few focused weeks of research and design up front can save you months, or years, of expensive misalignment down the line. The Opportunity Is Still Enormous Despite the industry's habit of chasing tech before thinking about people, I remain optimistic. The opportunity to differentiate in insurance is astounding. The bar for better experiences is still too low. AI can help agents spend less time searching and re-entering the same information. It can help newer employees get up to speed faster. It can preserve institutional knowledge, make complex decisions more transparent, and free up time for the things that actually build loyalty. Advice, empathy, and relationship-building. But only if it is designed around people. The companies that get this right understand that technology alone does not create transformation. People do. It comes down to whether they trust the tool enough—and find it valuable enough—to actually use it. Insurance doesn't need more AI for the sake of AI. It needs AI that solves actual problems for the people doing the work, in the real flow of their day. That's the design challenge. And if the industry takes it seriously, it's also the clearest path to transformation. Josh Levine

精算影响：可能影响获客成本、佣金结构、客户适当性、销售误导和产品组合。

建议行动：

  1. 拆分渠道价值：新单规模、继续率、赔付经验、费用率和投诉率。
  2. 检查佣金或渠道政策变化是否改变产品利润测试假设。
  3. 把渠道行为纳入经验分析，避免只看总体赔付率或退保率。

今日学习点：复习渠道费用、继续率、佣金递延、适当性管理和销售质量指标。

可分享版本：Insurance Doesn’t Have an AI Problem...：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

2. Reinsurance sidecars remain key in 2026, as third-party capital deployment holds stable: Aon Securities

摘要：This content is copyright to www.artemis.bm and should not appear anywhere else, or an infringement has occurred. Reinsurance sidecars remain a key theme in 2026, with third-party capital deployment holding broadly stable despite a rapidly evolving landscape of perils and structures, according to Aon Securities, the broker-dealer and investment banking arm of the broking firm. Writing in the broker’s latest reinsurance market report, Aon Securities observed that increased investor appetite is helping […] Reinsurance sidecars remain key in 2026, as third-party capital deployment holds stable: Aon Securities was published by: www.Artemis.bm Our catastrophe bond deal directory Sign up for our free weekly email newsletter here .

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：Reinsurance sidecars remain key in 2026, as third-party capital deployment holds stable: Aon Securities：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

3. 'But the AI Told Me To Do It!'

摘要：'But the AI Told Me To Do It!' AI & Machine Learning ITL Editorial Team Fri, 07/10/2026 - 06:40 As AI reshapes decision-making in insurance, the industry faces a critical question: Who holds liability when algorithms influence consequential outcomes? July 14, 2026 Image I once wrote a presentation called: "It wasn't me. AI told me to do it!" At the time, it was half joke, half warning. Certainly feels less comical now. Every organization adopting AI is moving toward the same uncomfortable question. When an AI-assisted decision causes harm, who carries the liability? Clearly not the model. Models do not sign contracts, settle claims, bind risks, approve suppliers or accept regulatory responsibility. Maybe not the vendor, whose terms will usually say that you, the customer, remain responsible for how outputs are used. Not necessarily the employee, if they were using an approved tool in an approved process. Not necessarily the committee, if it says it relied on the employee's professional judgment. Everybody, somebody and nobody. This is not an anti-AI argument. I use AI. Most of us now do. In most cases it is harmless enough: drafting an email, summarizing a meeting, turning scrappy notes into something coherent. No sensible firm should build a heavy governance process around every prompt. That would be maddening and almost unenforceable. But some uses are different. If AI helps tidy up a launch invite, nobody cares. If it helps route a claim, draft an underwriting rationale, influence a supplier decision, interpret a compliance obligation or shape a board paper, then we are in different territory. We are talking about authority. Insurance already understands authority. A junior underwriter cannot bind whatever they like. A claims handler has limits. A TPA works within a delegated claims authority. A coverholder operates within a binder. If something goes wrong, the questions are familiar: who had authority, what was the limit, was the decision escalated, and where is the evidence? AI does not change those principles. It just makes them harder to see. Take claims. An AI tool triages first notice of loss, summarizes the facts and recommends settlement within a low-value authority band. A handler reviews the screen and clicks through. Months later, a pattern emerges: the tool has been routing a class of claims too generously, too harshly, or inconsistently with the carrier's authority schedule. At that point, the question is not simply whether the model was accurate. It is whether the settlement sat within authority, who accepted it, whether the handler actually reviewed it, and whether the firm can produce a record created at the time rather than a reconstruction after the complaint lands. The same issue appears in delegated underwriting. An MGA uses AI to draft endorsements, referrals or risk summaries. The final document may still pass through a human. But did the output stay within binder authority? Did the right person approve it? Could a coverholder audit see the trail without piecing it together from emails and meeting notes? These are not exotic technology questions. They are ordinary insurance questions, just wearing new clothes. The problem for underwriters is that today's AI conversation is still too blunt. A proposal form might ask, "Do you use AI?" The insured says yes. Another insured says yes. Both attach an AI policy. On paper, they look broadly similar. But they may be completely different risks. One firm may let staff paste AI-generated analysis straight into consequential decisions with little more than a policy telling them to be careful. Another may classify the decision, check the user's authority, require escalation, capture sign-off and preserve the evidence. Those firms should not be priced as though they are the same. At the moment, they might be. This is because a policy is not proof. Training is not proof. A statement that "humans remain accountable" is useful, but only if you can identify the human, the decision, the authority and the record. The missing artefact is a decision record. For an AI-assisted decision that matters, an insurer should be able to ask: who requested the output, what was it used for, did the person have authority, was it escalated where needed, who accepted responsibility, and can the firm prove all this without reverse-engineering the story later? That last part matters. After a loss, everyone becomes a process expert. People remember the governance policy. They remember the meeting. They remember the human in the loop. But insurance does not work on vibes. It works on evidence. The answer, in my view, is not more slogans about responsible AI. It is the boring stuff insurance has always understood: authority, escalation, sign-off and evidence. The 95% of routine AI use should stay fast. Let people summarize, draft and explore. But the consequential minority needs a different track. If an output is going to move money, affect a customer, change a risk position, influence a regulatory judgment or commit the firm, someone has to own it. Not in theory. Not in a policy. In the record. "It wasn't me. AI told me to do it" may work as a joke in a presentation. It will not work in a claim file. The firms that can show who made the decision, who had authority and what evidence exists will look different from the firms that cannot. At some point, insurers will price that difference. The only question is whether they do it before the first major AI-accountability claim forces the issue. Julian Tranter

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：'But the AI Told Me To Do It!'：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

4. A Founder's Guide to Surviving Investor Rejection

摘要：A Founder's Guide to Surviving Investor Rejection Leadership ITL Editorial Team Mon, 07/13/2026 - 10:41 At 66, a cybersecurity veteran trades retirement planning for startup building and learns that success doesn't depend on yeses; it requires "not no"'s. July 13, 2026 Image One of my favorite movie scenes comes from "Volunteers." Tom Hanks is trying to negotiate with a local warlord. Standing nearby is the warlord's beautiful bodyguard—whose command of English is somewhere between nonexistent and interpretive dance. Tom flashes a grin that suggests he'd be perfectly happy if she happened to be part of the bargain. The warlord responds with something to the effect of, "If I say yes… and not no…" I honestly don't remember exactly how the scene ended. What I remember is what popped into my own head. I'd settle for not no. At the time, it was just a funny line. Thirty years later, after more investor meetings than I care to count, I finally understand why it stuck with me. Founders spend years chasing "yes." Investors rarely give you one. Instead they say… "Interesting." "Come back after revenue." "Let's reconnect in six months." "We'd like to see your next release." "Keep us posted." None of those are yes. But they aren't no. If you're building a company, you eventually realize that companies aren't built on yes. They're built on not no . The High Wire Being a founder is the proverbial high-wire act. There's no safety net. No guarantee. No instruction manual. People love talking about entrepreneurial risk. Let me save you some time. It's all risky. The right decisions. The wrong decisions. The crazy decisions. Sometimes you don't know which one you made until two years later. Then there are the mornings. 3 a.m. Every. Single. Morning. Not because the alarm went off. Because your brain did. There's always one more investor to research. One more slide to improve. One more grant proposal to edit. One more feature to design. One more email to send before the day job begins. People think founders work 80-hour weeks. The truth is… founders never really stop working. The company follows you to bed. It wakes up before you do. And then there's that feeling. If you've ever built a company, you know exactly what I'm talking about. That knot in the pit of your stomach. It never completely goes away. It's there when you wake up. It's there during investor meetings. It's there while you're brushing your teeth. It whispers the same questions over and over. What did I forget? Are we going to make it? Am I asking my family to believe in something impossible? Is this the dumbest thing I've ever done… or the smartest? I've come to think of it as the founder's tax. Nobody talks about it. Everybody pays it. Some people call it stress. Founders call it Tuesday. Venture Capitalists and Sea Turtles One of my favorite startup metaphors comes from Silicon Valley . Ron LaFlamme, the eccentric attorney, explains venture capital using sea turtles. Sea turtles lay hundreds of eggs because only one or two eventually make it to the ocean. "That's what Peter Gregory is doing," Ron explains. "Making sure one or two of his compression plays make it to the sea." The first time I heard that I remember thinking, "Why not just pick stronger turtles?" Of course, that's not how venture capital works. They're playing portfolio math. Fund enough companies and one eventually becomes the next Google. They're not looking for certainty. They're looking for outliers. Founders don't have that luxury. Most of us get one turtle. One company. One dream. One shot. It's amazing how differently you look at risk when you're carrying your only turtle. Government Grants: The Ultramarathon If raising venture capital is a marathon… government grants are an ultramarathon. Uphill. Into the wind. Dragging a filing cabinet behind you. You spend six weeks writing. Three weeks editing. Two weeks wondering whether Requirement 3.2.17(b) means exactly what you think it means. You finally hit "Submit." Then… absolutely nothing. Weeks become months. Months become more months. Eventually an email arrives. Your pulse quickens. Your palms get sweaty. You open it. "Thank you for your interest…" That's government-speak for, "Better luck next time." The amazing part? You immediately start writing the next proposal. Founders are funny that way. The government didn't invent persistence. Entrepreneurs did. Accelerators I actually like accelerators. Some of them. Many provide genuine value. They introduce founders to investors. They surround you with experienced entrepreneurs. They shorten the learning curve. Some absolutely earn the equity they receive. Others… Well… Let's just say the first image that came to my mind was a skinny kid explaining proper deadlifting technique to a professional bodybuilder. It made me laugh. Mostly because I've been there. Now before anyone gets offended… No, I don't know everything. Far from it. But this ain't Marine Corps boot camp. I don't need somebody teaching me how to polish my boots. I've spent decades leading soldiers, briefing executives, running cybersecurity organizations, and solving difficult problems. Teach me something I don't know. Introduce me to someone I couldn't otherwise meet. Open a door that's been closed. Challenge my assumptions. That's acceleration. Teaching me how to center a title on a PowerPoint slide? Not so much. Now, to be fair, accelerators usually introduce you to investors. Of course, they don't do it out of the goodness of their hearts. They generally take a slice of your company. Sometimes it's a reasonable slice. Sometimes… It's a fat butcher's slice. Every founder has to answer the same question. Was it worth it? If the answer is yes… great. If not… that was one expensive PowerPoint lesson. The Founder's Retirement Plan Somewhere along this journey I stopped looking at my investment portfolio as retirement. I see software development. Advertising. Patent attorneys. Trade shows. Cloud hosting. Developers. My financial advisor sees diversification. I see operating capital. Retirement? I'll think about retirement after Version 5.0 ships. Every now and then I tell Suzanne we're flying first class to the Maldives for a week of scuba diving. Just as soon as… well… just as soon as we can afford a margarita machine. Fans of "Silicon Valley" will appreciate that reference. Everyone else probably thinks I've developed an unhealthy obsession with frozen drinks. They're not entirely wrong. The funny thing about founders is that we stop measuring wealth the way everyone else does. A new car? That's six months of development. Kitchen remodel? Marketing budget. Vacation? Another developer. People ask how founders keep funding their companies. Simple. We stop thinking about assets. We start thinking about runway. Yin and Yang People ask what it's like to build a company with my wife. The answer usually surprises them. We work remarkably well together. Mostly because we work remarkably well apart. Ron LaFlamme would probably describe us as yin and yang. That's us. I'm the dreamer. Suzanne is the realist. I see possibilities. She sees details. I chase ideas. She quietly points out the 17 reasons one of them probably won't work. She's usually right. Long before software, we bought a short-term rental. The number one comment from our guests wasn't the location. It wasn't the view. It wasn't the amenities. It was one word. "Immaculate." That's Suzanne. If NASA hired her, astronauts would dust the launch pad before liftoff. She has standards that make hotel inspectors nervous. Thank goodness. Somebody has to. Every founder needs someone willing to ask, "Are you sure?" Not because they doubt the dream. Because they want the dream to survive. People celebrate founders. They should spend more time celebrating the people who quietly make founders better. The Turtle on the Fence Post There's an old saying: "If you see a turtle on a fence post, you know it didn't get there by itself." How he got up there is anybody's guess. Yes… I'm mixing metaphors. It's my article. Besides, if you've ever started a company, you know reality stopped making sense a long time ago. You stop measuring life normally. Your retirement account becomes software development. Vacation becomes cloud hosting. Credit cards become temporary venture capital. Your dog starts recognizing the Amazon delivery driver by first name. Normal people call this insanity. Founders call it product-market fit. The truth is, nobody builds a company alone. Somebody always believed. Somebody always introduced you to someone. Somebody always opened a door. And if you're lucky enough to succeed… maybe someday you'll become the person holding the door open for the next founder trying to get through. That's a legacy, too. Why 66? People may someday ask me a simple question. "Why did it take until you were 66?" It's a fair question. The funny thing is… I don't think I waited until I was 66 to become a founder. I think I spent 40 years accidentally preparing to become one. The Army taught me leadership. It also taught me that no plan survives first contact. Corporate America taught me patience. Cybersecurity taught me skepticism. Attackers adapt. Technology changes. Certainty is usually an illusion. Marriage taught me partnership. Investors taught me persistence. Government grants taught me humility. And rejection… Rejection taught me that success usually belongs to the person willing to hear "no" one more time than everyone else. Looking back, every assignment, every promotion, every setback, every impossible deadline, every deployment, every conference room, every board presentation, every sleepless night somehow led here. Maybe the company wasn't waiting for me. Maybe I was waiting to become the person capable of building the company. The Founder Nobody Sees People see the pitch. They see the product. They see the trade show booth. They see the LinkedIn announcement. What they don't see… is the founder sitting at the kitchen table at 3 a.m. trying to get two hours of work done before heading to the day job. They don't see weekends disappear. They don't see vacations turn into strategy sessions. They don't see the credit card bill arrive. They don't see another investor politely explaining why your company isn't quite ready. They don't see the quiet conversations between spouses. "Can we keep doing this?" "How much longer?" "Are we crazy?" The answer, by the way… is yes. Founders are a little crazy. Thankfully. If they weren't, most companies would never exist. Looking Forward Instead of Backward At 66, something changes. You stop asking, "How much money can I make?" You start asking, "What am I going to leave behind?" Money is nice. Don't misunderstand me. I'd love to stop looking at every block of stock in my retirement account as another software release or another attorney. I'd love to finally buy that margarita machine. I'd really love to take Suzanne to the Maldives and spend a week underwater instead of under deadlines. But that's not why I'm doing this. If our company succeeds, I hope my legacy isn't the software. I hope it isn't the patent. I hope it isn't the valuation. I hope it's the organization that never became tomorrow's headline because somebody finally started looking through the windshield instead of the rearview mirror. For decades, cybersecurity has become remarkably good at explaining yesterday. Yesterday's ransomware. Yesterday's phishing campaign. Yesterday's breach. Yesterday's lessons learned. Those things matter. But they're history. I've always believed we could do more. What if we could help organizations think about tomorrow? Not with certainty. Not with magic. Not with a crystal ball. Just disciplined analysis. Patterns. Trends. Probabilities. Enough information to make one better decision before the next attack arrives. If we accomplish that… then every sleepless night was worth it. Every rejection. Every investor meeting. Every government grant proposal. Every conference. Every dollar we invested instead of spending on ourselves. Worth it. The Last Word The funny thing about entrepreneurship is that people think the story ends when an investor finally says yes. It doesn't. That's just the next chapter. The real story is everything that happened before anyone believed. The three o'clock mornings. The knot in your stomach. The day job that funded the dream. The spouse who quietly kept believing. The people who opened doors. The investors who didn't say yes… but thankfully didn't say no, either. Today we're still building. Still pitching. Still applying. Still hearing, "Come back later." We're still looking at retirement accounts and seeing software development. We're still laughing about margarita machines. We're still dreaming about the Maldives. We're still walking the high wire. And after all these years… I'd still settle for… not no. Because every once in a while… … "not no" becomes "yes." Epilogue Or maybe just the quiet refusal to quit. Every founder needs something that carries them through the investor meetings, the rejection emails, the three o'clock mornings, and that knot in the pit of the stomach that never quite goes away. Keep walking. Keep building. Keep believing. Because every once in a while… one little turtle actually makes it to the sea. I'm fortunate. When I need a reminder to keep going, I don't have to look very far. Timothy O'Neil

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：A Founder's Guide to Surviving Investor Rejection：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

5. Gallagher Re estimates global insured cat losses at $46bn for H1’26, 28% below 10-year average

摘要：This content is copyright to www.artemis.bm and should not appear anywhere else, or an infringement has occurred. Insured losses from natural catastrophe events across the globe in the first half of 2026 are estimated to have reached USD $46 billion, down on last year’s $84 billion and 28% below the 10-year average of $64 billion, according to reinsurance broker Gallagher Re’s H1 2026 Natural Catastrophe and Climate Report. At $46 billion, H1’26 […] Gallagher Re estimates global insured cat losses at $46bn for H1’26, 28% below 10-year average was published by: www.Artemis.bm Our catastrophe bond deal directory Sign up for our free weekly email newsletter here .

精算影响：可能改变自留额、再保成本、尾部风险资本和财险定价充足性。

建议行动：

  1. 复核当前再保结构下 1-in-100 与 1-in-200 损失情景。
  2. 比较不同自留额对利润波动、资本占用和再保费用的影响。
  3. 把近期灾害经验纳入下一轮定价或预算假设讨论。

今日学习点：复习 XL、quota share、aggregate cover 与巨灾模型输出。

可分享版本：Gallagher Re estimates global insured cat losses at $46bn for H1’26, 28% below 10-year average：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

6. It's a Wired, Wired, Wired, Wired World

摘要：It's a Wired, Wired, Wired, Wired World PaulCarroll Mon, 07/13/2026 - 18:43 When Norway won games during the World Cup, so many people jumped up and down that earthquake sensors picked up tremors in Oslo. The same was true when Mexico won games; tremors were detected in Guadalajara and other parts of the country. That's some impressive fan support. Vamonos, Mexico! Dra til, Norge! But detecting the tremors also required some very impressive sensors — of the sort that can help insurers increasingly head off injuries and property damage from earthquakes, wildfires, and floods by giving people advance notice of the impending trouble. Let's have a look. Earthquake sensors are top of mind for me because of the devastating quakes in Venezuela and because of the 5.6-magnitude quake in late June that shook parts of Northern California where I lived until recently. Sensors in Google phones managed to alert more than 11.4 million people in Venezuela that a major earthquake was coming, at least several seconds before they felt the impact, according to the New York Times , and as much as two minutes ahead of time. It's not clear how many lives were saved and injuries prevented — and the losses were devastating, with nearly 4,500 deaths confirmed from the 7.2- and 7.5-magnitude earthquakes — but many people surely managed to protect themselves by quickly taking cover. What Google is doing is intriguing, and potentially a model for other alert systems. Google has turned all its phones into sensors that take advantage of the fact that earthquakes create two types of waves, as part of a system that is available in nearly 100 countries. One type (P-waves) travels very fast but does little damage. The other (S-waves) does the vast majority of the damage but travels significantly more slowly. Google's phones detect the fast-arriving P-waves as they travel through the ground, and, when Google sees all phones in an area lighting up at once, it knows S-waves and rumbling are coming. It's rather like thunder and lightning. Google's phones see the lightning and can tell people that thunder is coming. (The obvious difference being that, in the case of earthquakes, the damage comes after the alert, while lightning is both the alert and the cause of damage.) The systems don't necessarily provide a lot of warning. P-waves travel at 5-6km/sec, while S-waves spread at 3-4km/sec. So you'd need to be perhaps 20 miles away from the epicenter to get five seconds of warning. People will need to be educated about what to do with those five seconds (drop, cover and hold on) and become accustomed to the idea of alerts, so they don't freeze when the warnings arrive. But the sensor network could still get a lot of people away from whatever might fall on them, even with little advance notice, and prevent other damage, too. A woman I know was on an on-ramp for I80 in Berkeley when the Loma Prieta earthquake hit Northern California in 1989. The on-ramp collapsed, dropping her 30 feet onto a pile of rubble. The collapse not only totaled her car, of course, but had her in and out of surgery for years, and left her traumatized from knowing how many people were crushed beneath her. With just five seconds notice, she would have been able to pull off the road and stop short of the elevated roadway. In the recent California quake, the governor's office bragged that the state's new early warning system had alerted more than 1 million residents before the shaking started in their area, drawing on feeds from some 600 sensors installed around the state. The system is also available in Oregon and Washington, and Apple offers a similar sort of alert system, drawing on sensors that others have installed. Insurers don't have a role to play in the development of networks like Google's and don't have to help with the sort of deployment of hard-wired sensors like those in California, but they can certainly assist with the education. Those that do will not only reduce injury claims but will earn good citizen points. At a time when insurers are looking for ways to engage with policyholders more often — not just when collecting premiums or paying claims — offering education about how to protect yourself seems like a promising avenue. Sensors that can detect wildfires before they get out of control are likewise becoming far more sophisticated and are being deployed on the ground, in the air, and in satellites. Personally, I'm most intrigued by what's happening with satellites, both because they can cover nearly unlimited territory, almost minute by minute, and because I believe in having others do as much work for me as possible. Google doesn't sell its phones on the basis that they'll detect earthquakes. People buy the phones for the obvious reasons, then Google adds a bit of software, et voila! A detection network is suddenly deployed. I think the same potential is there to add wildfire detection capabilities to the thousands of low-earth satellites that Elon Musk and others are deploying to facilitate communications. Let them pay for the expensive hardware and the launch, then add a camera and other forms of sensors that can look down and spot even small fires. Floods, thus far, require dedicated networks of sensors, but there's progress there, too, as Houston is showing. Cities are installing small, inexpensive sensors that monitor water levels constantly, which usually providing hours of warning about developing floods. Cities can also warn motorists in real time to avoid underpasses where water has collected. Because these networks of sensors can't just be piggybacked onto other hardware, progress can be slow — adoption remains spotty , for instance, in Central Texas even in the wake of the disastrous flood a year ago that killed 130 people, including 25 young girls and two counselors at a summer camp. But the technology is there and will continue to make inroads. A rule of thumb I developed some years ago now, as part of what I call the Laws of Zero, is that you can assume that any bit of information you want will be available to you at what looks like zero cost (compared with today) if you look down the road a ways. The concept is me looking for areas outside computer chips where the magic of Moore's law can apply. Moore's law — essentially, that the power of a computer processor doubles every year and a half to two years at no increase in cost — means that a unit of computing power that cost a dollar in 2000 costs roughly 1/600th of a penny today. So, free (almost) for anyone making long-range plans in 2000. I won't go into all seven of the areas I identified, but it's pretty easy to see how sensors fit the Laws of Zero pattern. Moore's law will drive the cost of the computing and any memory toward zero. WiFi and satellite connectivity are becoming ubiquitous, so there's no marginal communication cost. Batteries are also plunging in cost, and many sensors won't even need them, either because they can use solar power (whose cost is heading toward zero) or because they're built into bigger systems such as Google phones or Starlink satellites. The Law of Zero about sensors means we will keep seeing progress. Insurers won't even have to pay for that progress. They can just piggyback on what others are doing, then help policyholders understand how to take advantage of the progress — reducing claims while earning good will. In the meantime, if you aren't watching the France-Spain World Cup semifinal this afternoon, or at least sneaking the occasional peak while at work, I'll bet you'll be able to tell the result if you have access to seismograph readings from Paris and Madrid at 5pm or so Eastern time. Cheers, Paul Image Paul Carroll As sensors have demonstrated during the World Cup, the globe is becoming so wired that it's possible to spot earthquakes, wildfires, and floods in time to mitigate harm. July 13, 2026 Featured Commentary Off Is ITL Recommends Off

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：It's a Wired, Wired, Wired, Wired World：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

7. Biggest Threat Yet to Captive Insurance Agents

摘要：Biggest Threat Yet to Captive Insurance Agents PaulCarroll Mon, 07/06/2026 - 15:21 Back in 2013, when Chunka Mui and I were doing some consulting work on innovation for the CEO of a top-five personal lines insurer, he was trying to rewire the compensation structure for his captive agents. He wanted to encourage them to focus more on growth and less on building a book of business and then servicing it ("coasting," in his words). He noted that he wasn't trying to cut the total dollars paid to agents. He just wanted to take two percentage points out of the base commission and pay the money out as incentives. "But every time I float the idea," he said, "the agents turn around and kick me in the crotch." (He used a more colorful word.) Having kept an eye on the issue for more than a dozen years now, I believe that State Farm's announcement of a take-it-or-leave-it, incentive-driven compensation model for its 19,000 captive agents marks a turning point. Change always takes time, but I believe the captive agent business will be very different a few years from now. Let's have a look. A smart piece by David Gritz of InsurTech NY provides the backdrop, showing how the industry has been deemphasizing the traditional captive model for years. Noting that the trend predates the generative AI explosion by many years, he writes: "June 2020: Nationwide ends its captive agent program. "November 2021: Liberty Mutual transitions captive agents to independent agencies. "January 2023: Allstate signals a reduction in captive distribution. "June 2026: State Farm reduces benefits and commissions for captive agents. "Viewed individually, each decision can be explained by company-specific circumstances. Viewed together, they reveal something larger: carriers are increasingly questioning whether exclusive distribution remains the optimal model for growth." Gritz also neatly summarizes what, for me, is the core change that is working against captive agents: "Consumers can purchase insurance through direct channels, comparison platforms, embedded insurance experiences, independent agencies, affinity groups, digital marketplaces, MGAs, and increasingly AI-powered interfaces. "Carriers want the flexibility to pursue all of these opportunities simultaneously. Exclusive distribution creates natural channel conflict when a carrier wants to experiment with new distribution strategies." He gets into other reasons, too, but for me the key is that three decades of development of the internet, led by customer service pioneers such as Amazon, have conditioned us to expect to be able to see all our options, and instantly. We don't just look at what clothes Macy's or Nordstrom might offer us; we look at every seller. Even if we've settled on a brand or a specific item, we still look everywhere for the best prices — in seconds. In that sort of world, it just doesn't make sense for someone looking for insurance to walk into the office of the local State Farm agent, even if the agent is a smart and lovely person who sponsors the customer's daughter's soccer team. It's not clear how quickly the change away from captive agents will happen. A Silicon Valley truism is that you have to make sure you don't confuse a clear view with a short distance. And the reason for that adage is that so many people make that exact mistake all the time — including, well, me. I predicted the end of car dealerships 25 years ago, because all you really need is a way to test drive a car. You can then order your choice straight from the manufacturer, get it in a couple of weeks, and not have billions of dollars of car inventory sitting on lots around the country, pushing up costs for everybody. But change has been so slow that we're only now starting to see the sorts of effects on dealers that I expected by 2005 or 2010. Still, the transition away from captive agents is inevitable. Independent agents will keep growing — witness the interest in the HUB International IPO — while captive agents will have to fight a rear guard action. They will be under pressure from both ends. Their carrier employers will demand more growth and more flexibility to explore other distribution channels. Customers will press for lower prices while also insisting on more options. And I think State Farm, as one of the last big holdouts relying on captive agents, has pushed the transition past the tipping point, so it should only accelerate from here. Cheers, Paul Image Paul Carroll State Farm's announcement of a tough new compensation structure suggests that the captive model for insurance agents has finally passed a tipping point. July 7, 2026 Featured Commentary Off Is ITL Recommends Off

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：Biggest Threat Yet to Captive Insurance Agents：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

8. PERILS raises Victoria bushfire insured market loss estimate to AU$860m

摘要：This content is copyright to www.artemis.bm and should not appear anywhere else, or an infringement has occurred. Catastrophe data aggregator PERILS has published its third industry loss estimate for the Victoria Bushfires which took place during the period of January 7th to 13th, 2026, with the insured industry loss now standing at AU$860 million. PERILS published its initial industry loss estimate for the event in late February, six weeks after the event […] PERILS raises Victoria bushfire insured market loss estimate to AU$860m was published by: www.Artemis.bm Our catastrophe bond deal directory Sign up for our free weekly email newsletter here .

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：PERILS raises Victoria bushfire insured market loss estimate to AU$860m：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

9. 大家财险挂牌卖楼，公司2025年车险业务承保亏损5500万 - 东方财富

摘要：大家财险挂牌卖楼，公司2025年车险业务承保亏损5500万 东方财富

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：大家财险挂牌卖楼，公司2025年车险业务承保亏损5500万 - 东方财富：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

10. 不再独宠“宁王” 新能源车企集体为供应链“上保险”，动力电池供应体系正向“双供应”“多供应”演进 - 新浪财经

摘要：不再独宠“宁王” 新能源车企集体为供应链“上保险”，动力电池供应体系正向“双供应”“多供应”演进 新浪财经

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：不再独宠“宁王” 新能源车企集体为供应链“上保险”，动力电池供应体系正向“双供应”“多供应”演进 - 新浪财经：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

11. 巴掌大剐蹭赔1万!2026新能源车隐性成本彻底曝光_小猪仔仔 - 新浪汽车

摘要：巴掌大剐蹭赔1万!2026新能源车隐性成本彻底曝光_小猪仔仔 新浪汽车

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：巴掌大剐蹭赔1万!2026新能源车隐性成本彻底曝光_小猪仔仔 - 新浪汽车：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

12. ILS provides crucial portfolio diversification ahead of Super El Niño: VP Bank’s Allgäuer

摘要：This content is copyright to www.artemis.bm and should not appear anywhere else, or an infringement has occurred. Whilst it appears that a super El Niño could be on the horizon, a new commentary from VP Bank AG authored by Senior Investment Strategist Bernhard Allgäuer indicates that insurance-linked securities (ILS) can provide portfolio diversification as their returns are driven by natural disasters, rather than financial market turmoil, making them largely uncorrelated with traditional […] ILS provides crucial portfolio diversification ahead of Super El Niño: VP Bank’s Allgäuer was published by: www.Artemis.bm Our catastrophe bond deal directory Sign up for our free weekly email newsletter here .

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：ILS provides crucial portfolio diversification ahead of Super El Niño: VP Bank’s Allgäuer：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

13. People Moves: Everest Names Hanrahan Global Distribution & Chief Commercial Officer

摘要：Everest Group Ltd., headquartered in Hamilton, Bermuda, appointed Craig Hanrahan as Global Distribution & Chief Commercial Officer, effective August 1, 2026. He will continue to report directly to Jim Williamson, Everest president and CEO, serving as a member of the …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：People Moves: Everest Names Hanrahan Global Distribution & Chief Commercial Officer：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

14. US P/C Industry Books Best Result in a Decade but Not All Lines Enjoy Success

摘要：The U.S. property/casualty industry posted its best underwriting profit and combined ratio in a decade in 2025, building upon the $1 trillion of total direct premiums written logged the prior year for the first time. According to a new report …

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：US P/C Industry Books Best Result in a Decade but Not All Lines Enjoy Success：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

15. 新能源专属车险到底保什么？3大保障维度一次说清+FAQ - 新浪网

摘要：新能源专属车险到底保什么？3大保障维度一次说清+FAQ 新浪网

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：新能源专属车险到底保什么？3大保障维度一次说清+FAQ - 新浪网：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

16. Auto and Property Insurers Will Need to Maximize Retention Efforts to Stay Competitive in 2026 - TransUnion

摘要：Auto and Property Insurers Will Need to Maximize Retention Efforts to Stay Competitive in 2026 TransUnion

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：Auto and Property Insurers Will Need to Maximize Retention Efforts to Stay Competitive in 2026 - TransUnion：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

17. David Maslo appointed CEO of African Risk Capacity Ltd

摘要：This content is copyright to www.artemis.bm and should not appear anywhere else, or an infringement has occurred. African Risk Capacity Limited (ARC Ltd.) the financial affiliate and parametric insurance underwriting entity of the African Risk Capacity (ARC) Group, has appointed David Maslo as its permanent CEO, following his successful tenure as interim leader during a period of significant transformation for the company. In June 2025, the Board of ARC Ltd. appointed Maslo […] David Maslo appointed CEO of African Risk Capacity Ltd was published by: www.Artemis.bm Our catastrophe bond deal directory Sign up for our free weekly email newsletter here .

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：David Maslo appointed CEO of African Risk Capacity Ltd：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

18. 中华财险宜宾中支开展金融消保反诈主题宣传活动 - 中国日报网

摘要：中华财险宜宾中支开展金融消保反诈主题宣传活动 中国日报网

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：中华财险宜宾中支开展金融消保反诈主题宣传活动 - 中国日报网：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

19. 大家财险出售深圳总部附近两处商业地产 - 财新

摘要：大家财险出售深圳总部附近两处商业地产 财新

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：大家财险出售深圳总部附近两处商业地产 - 财新：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

20. 全力护航“车轮经济” 人保财险推出“人保优选 让修车更放心”服务 - 新华网客户端

摘要：全力护航“车轮经济” 人保财险推出“人保优选 让修车更放心”服务 新华网客户端

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：全力护航“车轮经济” 人保财险推出“人保优选 让修车更放心”服务 - 新华网客户端：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

21. One Weather Firm Warns That New England Could See Big Hurricane This Season

摘要：All of the best-known storm forecasting services have predicted a mild 2026 Atlantic hurricane season. That includes Colorado State University, which last week revised its outlook to “well below normal” with just nine named storms, four hurricanes, and only one …

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：One Weather Firm Warns That New England Could See Big Hurricane This Season：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

22. Markets/Coverages: Lemonade Enters Maine and Vermont Renters Insurance Markets

摘要：Digital insurer Lemonade has expanded availability of its renters insurance into Vermont and Maine. They become the 43rd and 44th states where Lemonade offers renters insurance. Lemonade lets renters use an app to obtain quotes, purchase policies, update existing policies, …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：Markets/Coverages: Lemonade Enters Maine and Vermont Renters Insurance Markets：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

23. Keystone Pipeline System’s Operator to Pay $26.9M Penalty Over Kansas Oil Spill

摘要：A proposed legal settlement with the U.S. government would require the Keystone Pipeline system’s operator to pay a $26.9 million civil penalty over a major oil spill in Kansas in December 2022 and spend about $40 million more to prevent …

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：Keystone Pipeline System’s Operator to Pay $26.9M Penalty Over Kansas Oil Spill：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

24. $25 million lawsuit over paid leave as an accommodation dismissed

摘要：A federal court on Monday dismissed a Department of Homeland Security special agent’s disability discrimination and retaliation lawsuit, ruling that paid medical leave constituted a reasonable accommodation following spinal surgery.

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：$25 million lawsuit over paid leave as an accommodation dismissed：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

25. Munich Re highlights its global reinsurance role as investors eye long-term risk trends - Ad-hoc-news.de

摘要：Munich Re highlights its global reinsurance role as investors eye long-term risk trends Ad-hoc-news.de

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：Munich Re highlights its global reinsurance role as investors eye long-term risk trends - Ad-hoc-news.de：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

26. 7.8|平安健康险宜昌中支联合国华人寿宜昌中支开展2026年7·8全国保险公众宣传日活动- 湖北日报新闻客户端 - 湖北日报传媒集团

摘要：7.8|平安健康险宜昌中支联合国华人寿宜昌中支开展2026年7·8全国保险公众宣传日活动- 湖北日报新闻客户端 湖北日报传媒集团

精算影响：可能影响定价假设、续保管理、赔付率监控和准备金充足性。

建议行动：

  1. 拉取最近 12 个月赔付率、件均赔款、出险频率和续保率趋势。
  2. 按年龄、地区、责任和渠道拆分经验偏差，找出主要驱动因素。
  3. 做赔付率上升 5%/10% 对利润和准备金的敏感性测试。

今日学习点：复习赔付率、医疗通胀、发病率、续保率与风险选择。

可分享版本：7.8|平安健康险宜昌中支联合国华人寿宜昌中支开展2026年7·8全国保险公众宣传日活动- 湖北日报新闻客户端 - 湖北日报传媒集团：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

27. How the Insurance Industry is Navigating the Complex Debate Over Fairness in Pricing - Risk & Insurance

摘要：How the Insurance Industry is Navigating the Complex Debate Over Fairness in Pricing Risk & Insurance

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：How the Insurance Industry is Navigating the Complex Debate Over Fairness in Pricing - Risk & Insurance：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

28. UK Sets Out Rules to Create Captive Insurance Regime

摘要：The UK has proposed rules to help encourage a potential new insurance market worth billions of pounds as part of a drive to boost competitiveness. The Bank of England and the Financial Conduct Authority laid out plans for a captive …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：UK Sets Out Rules to Create Captive Insurance Regime：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

29. New MD of real estate and heavy industry – multinational at Price Forbes

摘要：Darren Ting has been appointed managing director of real estate and heavy industry – multinational at Price Forbes, effective from June 2027. In his new role, he will be responsible for leading the development and build-out o... Want to read this article? Register for ultimate access to this article and …

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：New MD of real estate and heavy industry – multinational at Price Forbes：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

30. 银行业保险业加强网络安全监管 - 人民日报

摘要：银行业保险业加强网络安全监管 人民日报

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：银行业保险业加强网络安全监管 - 人民日报：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

31. 金融监管总局拟加强银行业保险业网络安全管理 - thecover.cn

摘要：金融监管总局拟加强银行业保险业网络安全管理 thecover.cn

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：金融监管总局拟加强银行业保险业网络安全管理 - thecover.cn：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

32. 多地暴雨灾害保险赔付已超22亿元 监管要求能赔快赔、应赔尽赔、合理预赔 - 财新

摘要：多地暴雨灾害保险赔付已超22亿元 监管要求能赔快赔、应赔尽赔、合理预赔 财新

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：多地暴雨灾害保险赔付已超22亿元 监管要求能赔快赔、应赔尽赔、合理预赔 - 财新：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

33. EU insurance watchdog warns against ‘race to the bottom’ on capital rules - Luxembourg Times

摘要：EU insurance watchdog warns against ‘race to the bottom’ on capital rules Luxembourg Times

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：EU insurance watchdog warns against ‘race to the bottom’ on capital rules - Luxembourg Times：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

34. How AI is changing the insurance claims process and what it means for accident victims - InsuranceNewsNet

摘要：How AI is changing the insurance claims process and what it means for accident victims InsuranceNewsNet

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：How AI is changing the insurance claims process and what it means for accident victims - InsuranceNewsNet：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

35. 2026 global insurance outlook - Deloitte

摘要：2026 global insurance outlook Deloitte

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：2026 global insurance outlook - Deloitte：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

36. “奋进十五五 保险有底气”人保寿险日喀则中支普及金融保险知识 - 中国日报网

摘要：“奋进十五五 保险有底气”人保寿险日喀则中支普及金融保险知识 中国日报网

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：“奋进十五五 保险有底气”人保寿险日喀则中支普及金融保险知识 - 中国日报网：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

37. 聚力“7·8”守初心 保险赋能惠巴蜀——人保寿险四川省分公司开展保险公众宣传日系列活动 - 四川新闻

摘要：聚力“7·8”守初心 保险赋能惠巴蜀——人保寿险四川省分公司开展保险公众宣传日系列活动 四川新闻

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：聚力“7·8”守初心 保险赋能惠巴蜀——人保寿险四川省分公司开展保险公众宣传日系列活动 - 四川新闻：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

38. 惠民宣传进街巷保险服务暖民心——人保寿险郴州中支城区本部开展7·8全国保险公众宣传日活动 - 中宏网

摘要：惠民宣传进街巷保险服务暖民心——人保寿险郴州中支城区本部开展7·8全国保险公众宣传日活动 中宏网

精算影响：值得持续观察其对产品、风险、资本和客户行为的间接影响。

建议行动：

  1. 记录它可能影响的精算假设，并标注需要哪些数据验证。
  2. 找一个同业或历史案例做对照，判断这是一日新闻还是长期趋势。
  3. 把结论写成三句话，沉淀到个人知识库。

今日学习点：练习把行业事件翻译成假设、现金流、资本和利润四个维度。

可分享版本：惠民宣传进街巷保险服务暖民心——人保寿险郴州中支城区本部开展7·8全国保险公众宣传日活动 - 中宏网：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

39. India’s Largest Nuclear Power Plant Hit by Data Breach

摘要：Ransomware group World Leaks has posted on the dark web a huge cache of files related to India’s largest nuclear plant, including purported blueprints of parts of its facilities and supplier details — information it labeled as coming from Reliance …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：India’s Largest Nuclear Power Plant Hit by Data Breach：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

40. After Losing Job and Crypto, Man Falsely Claimed $1.3M From 107 Class Actions

摘要：An upstate New York man has been accused of receiving more than 27,000 payments totaling more than $1.3 million from approximately 107 different class action lawsuit settlements between January 2022 and December 2025. According to court documents, the man admitted …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：After Losing Job and Crypto, Man Falsely Claimed $1.3M From 107 Class Actions：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

41. Texas Contractor Cited After Worker Dies in Elementary School Crawl Space

摘要：The U.S. Department of Labor has cited a building contractor and a staffing company for safety violations after a worker suffered fatal injuries while operating a mini-excavator beneath an elementary school in Converse, Texas. The department’s Occupational Safety and Health …

精算影响：可能影响定价假设、续保管理、赔付率监控和准备金充足性。

建议行动：

  1. 拉取最近 12 个月赔付率、件均赔款、出险频率和续保率趋势。
  2. 按年龄、地区、责任和渠道拆分经验偏差，找出主要驱动因素。
  3. 做赔付率上升 5%/10% 对利润和准备金的敏感性测试。

今日学习点：复习赔付率、医疗通胀、发病率、续保率与风险选择。

可分享版本：Texas Contractor Cited After Worker Dies in Elementary School Crawl Space：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

42. Markel targets lead position on complex construction project risks

摘要：Markel launched a new construction and engineering practice in London towards the end of last year. Adrian Ladbury interviews Anna Woolley, director – construction and engineering at Markel International, about the reasons fo... Want to read this article? Register for ultimate access to this article and ALL our premium content …

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：Markel targets lead position on complex construction project risks：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

43. DUAL Group launches global transactional risk offering

摘要：A new global transactional risk offering has been launched by DUAL Group, with long-term global backing led by Liberty. DUAL said the new offering combines its expertise across warranty & indemnity/reps & warranties, ... Want to read this article? Register for ultimate access to this article and ALL our premium …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：DUAL Group launches global transactional risk offering：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

44. Aviva and ONYX Insight collaborate on turbine maintenance analytics

摘要：Aviva’s Global Risk Management Solutions has partnered with ONYX Insight, a provider of predictive analytics for the global wind industry, on a project designed to enable wind asset owners and operators to take a proactive ap... Want to read this article? Register for ultimate access to this article and ALL …

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：Aviva and ONYX Insight collaborate on turbine maintenance analytics：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

45. Simpson Thacher adds three partners to insurance team

摘要：New York-based Simpson Thacher and Bartlett added three partners to its insurance transactional and regulatory team.

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：Simpson Thacher adds three partners to insurance team：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

46. Munich Re Specialty launches miner rescue coverage

摘要：Munich Re Specialty has launched a Lloyd’s of London market consortium for miner rescue insurance.

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：Munich Re Specialty launches miner rescue coverage：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

47. 《金融产品网络营销管理办法》对保险网络营销的秩序重塑 - Lexology

摘要：《金融产品网络营销管理办法》对保险网络营销的秩序重塑 Lexology

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：《金融产品网络营销管理办法》对保险网络营销的秩序重塑 - Lexology：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

48. 金融监管总局：警惕借“保险客户旅游”名义侵害消费者权益 - thepaper.cn

摘要：金融监管总局：警惕借“保险客户旅游”名义侵害消费者权益 thepaper.cn

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：金融监管总局：警惕借“保险客户旅游”名义侵害消费者权益 - thepaper.cn：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

49. 比亚迪搞的这个辅助驾驶兜底还是很猛的，它是独立于常规保险体系的。... - 汽车之家

摘要：比亚迪搞的这个辅助驾驶兜底还是很猛的，它是独立于常规保险体系的。... 汽车之家

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：比亚迪搞的这个辅助驾驶兜底还是很猛的，它是独立于常规保险体系的。... - 汽车之家：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

50. 50%汽服店亏损，车主被车企、4S店、抖音、保险公司四重截流，大量汽服单店没生意而亏损？ - 手机网易网

摘要：50%汽服店亏损，车主被车企、4S店、抖音、保险公司四重截流，大量汽服单店没生意而亏损？ 手机网易网

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：50%汽服店亏损，车主被车企、4S店、抖音、保险公司四重截流，大量汽服单店没生意而亏损？ - 手机网易网：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

51. Dear CEO Letter outlines enhanced supervisory focus on financial reporting, solvency oversight and data quality - MaltaToday

摘要：Dear CEO Letter outlines enhanced supervisory focus on financial reporting, solvency oversight and data quality MaltaToday

精算影响：可能影响资本占用、偿付能力充足率、风险偏好和业务增长空间。

建议行动：

  1. 检查最近一期偿付能力指标对利率、赔付率和退保率的敏感性。
  2. 列出资本消耗最高的产品线，并标注可调整的定价或再保杠杆。
  3. 准备一个 25bp/50bp 利率下行情景下的资本影响小测算。

今日学习点：复习最低资本、实际资本、风险资本和偿付能力充足率。

可分享版本：Dear CEO Letter outlines enhanced supervisory focus on financial reporting, solvency oversight and data quality - MaltaToday：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

52. Climate litigation risk already here for banks and insurers, says ECB legal chief - Green Central Banking

摘要：Climate litigation risk already here for banks and insurers, says ECB legal chief Green Central Banking

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：Climate litigation risk already here for banks and insurers, says ECB legal chief - Green Central Banking：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

53. NAIC Financial Condition (E) Committee Approves RBC Changes - Mayer Brown

摘要：NAIC Financial Condition (E) Committee Approves RBC Changes Mayer Brown

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：NAIC Financial Condition (E) Committee Approves RBC Changes - Mayer Brown：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

54. Regulators clear way to rewrite annuity illustration rules - InsuranceNewsNet

摘要：Regulators clear way to rewrite annuity illustration rules InsuranceNewsNet

精算影响：可能改变产品备案、信息披露、销售合规或风险治理口径。

建议行动：

  1. 整理一页影响清单：涉及产品、模型、报告、流程和负责人。
  2. 检查近期产品备案、精算报告和销售材料是否存在需要同步更新的口径。
  3. 把监管要求拆成必须做、建议做、持续观察三类任务。

今日学习点：复习监管资本、产品备案、信息披露与消费者保护之间的关系。

可分享版本：Regulators clear way to rewrite annuity illustration rules - InsuranceNewsNet：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

55. 30 High-Paying Remote Jobs With $100,000 (or Higher) Salaries - Money Talks News

摘要：30 High-Paying Remote Jobs With $100,000 (or Higher) Salaries Money Talks News

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：30 High-Paying Remote Jobs With $100,000 (or Higher) Salaries - Money Talks News：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

56. How Rated Note Feeders Help Insurers Tap Private Credit - Law360

摘要：How Rated Note Feeders Help Insurers Tap Private Credit Law360

精算影响：保险风险正在和银行资本、投行资产、车企生态、能源转型、医疗服务或数据中心投资互相传导。

建议行动：

  1. 画出联动链条：对手方、资产端、负债端、销售渠道和客户行为。
  2. 判断风险落点：信用风险、市场风险、承保风险、操作风险还是声誉风险。
  3. 找一个可量化指标跟踪，例如违约率、维修成本、医疗通胀、赔付频率或资产久期。

今日学习点：复习资产负债联动、信用风险迁移、生态渠道和保险服务嵌入式销售。

可分享版本：How Rated Note Feeders Help Insurers Tap Private Credit - Law360：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

57. AIG names Nancy Bewlay global chief underwriting officer - Insurance Business

摘要：AIG names Nancy Bewlay global chief underwriting officer Insurance Business

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：AIG names Nancy Bewlay global chief underwriting officer - Insurance Business：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

58. Ghana Re puts treaty execution and Strait of Hormuz risk in focus at GIA seminar - Insurance Business

摘要：Ghana Re puts treaty execution and Strait of Hormuz risk in focus at GIA seminar Insurance Business

精算影响：可能改变核保、定价、理赔、反欺诈、客户运营和模型治理方式。

建议行动：

  1. 判断技术处于概念验证、试点、规模化还是监管约束阶段。
  2. 列出一个可落地场景：数据输入、模型输出、业务决策、风险控制。
  3. 检查模型风险：公平性、可解释性、漂移监控、人工复核和数据授权。

今日学习点：复习 GLM、机器学习定价、模型治理、反欺诈和可解释 AI。

可分享版本：Ghana Re puts treaty execution and Strait of Hormuz risk in focus at GIA seminar - Insurance Business：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

59. Insurance Insights May 2026 - kpmg.com

摘要：Insurance Insights May 2026 kpmg.com

精算影响：适合沉淀为长期研究主题、假设库、方法论模板或同业 benchmark。

建议行动：

  1. 提炼三条可复用结论：趋势、数据口径、方法论。
  2. 判断报告结论是否能转成模型假设、压力情景或管理层汇报图表。
  3. 把关键图表或指标加入个人知识库，并记录适用市场和限制条件。

今日学习点：复习经验研究、外部 benchmark、假设设定和管理层叙事。

可分享版本：Insurance Insights May 2026 - kpmg.com：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

60. Reinsurance Market Size, Share | Industry Report, 2034 - Fortune Business Insights

摘要：Reinsurance Market Size, Share | Industry Report, 2034 Fortune Business Insights

精算影响：可能改变自留额、再保成本、尾部风险资本和财险定价充足性。

建议行动：

  1. 复核当前再保结构下 1-in-100 与 1-in-200 损失情景。
  2. 比较不同自留额对利润波动、资本占用和再保费用的影响。
  3. 把近期灾害经验纳入下一轮定价或预算假设讨论。

今日学习点：复习 XL、quota share、aggregate cover 与巨灾模型输出。

可分享版本：Reinsurance Market Size, Share | Industry Report, 2034 - Fortune Business Insights：对精算人来说，关键不是新闻本身，而是它会怎样改变假设、现金流、资本和利润。

今日 15 分钟练习

选择上面任意一条信息，写出它对一个保险产品的四段式影响：假设、现金流、利润、资本。

明日可迭代方向