Frédéric Voyer

📘 Day 17 of 60 – Reinsurance Insights Series 🔁 Reinstatements in XoL Treaties – How Capacity is Restored After a Loss (And Why It’s Not Free!) In Excess of Loss (XoL) reinsurance, once the reinsured has exhausted the coverage limit due to a claim, the treaty doesn’t automatically restore itself for future claims. That’s where Reinstatements come in! 🛠️ What is a Reinstatement? A reinstatement allows the reinsured to restore the coverage after a loss, usually by paying an additional premium. Think of it as buying a top-up of the limit you’ve already used. ⸻ 🔍 Example • XoL Treaty Limit: $10M excess of $5M • Premium for 1st layer: $1M • Event 1 Loss: $10M → reinsurer pays $5M (above retention) • Event 2 Loss: $10M → no coverage unless reinstated ✅ With 1 free reinstatement, reinsured gets coverage again. 💸 Reinstatement Premium: If reinstatement rate is 100%, and $5M limit was used, premium = → $1M × (Used Limit / Full Limit) → $1M × ($5M / $10M) = $500K ⸻ 💡 Why It Matters: • Restores capacity after losses • Costs extra (usually pro-rata) • Can be free or charged, depending on treaty wording • Multiple reinstatements may be allowed (e.g., 1+1 or 2+2) ⸻ 🧠 Pro Tip: Reinstatements are not always guaranteed – they must be negotiated and included in the contract! 🔍 Drafted by ChatGPT with hands-on guidance from a reinsurance practitioner with deep domain experience. 📌 Up Next (Day 18): Let’s demystify Quota Share Treaties – the building block of proportional reinsurance, with a simple premium and claims split example. #Reinsurance #ReinstatementPremium #ExcessOfLoss #Insurance #RiskManagement #Retrocession #ReinsuranceEducation #LinkedInLearning #Underwriting #CatastropheCover #FacultativeReinsurance

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*A new approach in two-dimensional heavy-tailed distributions* by Konstantinides Dimitrios, Charalampos Passalidis – published in the July 2025 issue of the Annals of Actuarial Science. Link to full text in the comments. Abstract: We consider a new approach in the definition of two-dimensional heavy-tailed distributions. Specifically, we introduce the classes of two-dimensional long-tailed, of two-dimensional dominatedly varying, and of two-dimensional consistently varying distributions. Next, we define the closure property with respect to two-dimensional convolution and to joint max-sum equivalence in order to study whether they are satisfied by these classes. Further, we examine the joint-tail behavior of two random sums, under generalized tail asymptotic independence. Afterward, we study the closure property under scalar product and two-dimensional product convolution, and by these results, we extended our main result in the case of jointly randomly weighted sums. Our results contained some applications where we establish the asymptotic expression of the ruin probability in a two-dimensional discrete-time risk model. #actuarialscience #research #extremes #heavytails #ruin

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#Insurance today is under pressure from all sides — customers expecting Netflix-like experiences, #regulators tightening the screws, and competitors releasing new products at breakneck speed. The challenge is real: - How do you launch products faster without breaking compliance? - How do you give customers and partners self-service capabilities without bloating IT budgets? - How do you modernize legacy platforms while keeping the lights on? This is exactly where we at StatusNeo have been focusing our efforts recently — working with some of APAC’s leading insurers and FSI players to solve these very challenges. Our Playbook = Simple We take a business-first view of the problem: 1) Blueprint the “target state” – faster release cadences, better customer journeys, less tech debt. 2) Build high-velocity pods – small, outcome-driven squads that deliver production-grade releases in weeks, not months. 3) Lean on #accelerators, not brute force – our in-house IP reduces cycle times, automates the boring stuff, and frees up teams to focus on growth. In some of the most recent stints, working with Amazon Web Services (AWS) teams in Singapore and Australia, we’ve embedded #observability, #compliance, and #security from day zero — using #EKS, #Bedrock, #Step Functions, and Control Tower to create a #DevSecOps fabric that is as robust as it is scalable. The Business Outcomes Speak for Themselves: - 40–60% shorter product release cycles — so new policies hit the market faster - AI-powered self-service — so brokers and customers get answers in seconds - Reduced tech debt — so IT can focus on enabling growth instead of firefighting This isn’t just a “digital transformation” initiative — it’s helping #insurers move from reactive to proactive, building enterprises that are adaptive, autonomous, and always-on. P.S. Our #AI #Periodic #Table has been receiving rave reviews and, frankly, more requests than we expected. If you’d like a copy to see how we’re mapping the AI landscape for BFSI and beyond, just drop a comment or DM me — happy to share.

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🔍 RAG is not reasoning — and that matters when applying LLMs to insurance claims. We often hear: “Just use RAG to bring your data to GPT.” But in real-world claims work — where precision, policy logic, and privacy matter — it’s not that simple. Let’s take a workmen injury case: 👷 A scanned medical bill comes in. 💊 Diagnosis: left arm fracture, X-ray, and cast. 📄 Policy limit: $500 diagnostic, $1,000 treatment. Now the question is — can GPT-4 estimate the payable amount? And can it do that without data leakage? Here’s what I’ve learned: ✅ If you’re using Azure OpenAI, you can apply GPT-4 reasoning within your secure tenant. The LLM never sees your raw data outside your infra. ✅ If you want full control, open-source models like Kimi 2 or Mistral can run locally — great for proprietary underwriting rules. Think: actuarial logic, loss reserves, even claim precedent retrieval. ⚠️ But if you’re using RAG with general models, remember: • It’s smart retrieval, not fresh thinking. • Unless your claim logic already lives in the model’s weights, it might sound convincing — but get it completely wrong. 🔐 In insurance, hallucinations can cost money — or worse, break trust. Bottom line: You can use LLMs on claims data safely. But only if you own the pipeline, scrub the PII, and know what the model actually understands. Curious how others are tackling this? Happy to swap notes on private LLM setups or local RAG architectures. #LLM #RAG #InsuranceAI #ClaimsAutomation #DataPrivacy #GenAI #Kimi2 #AzureOpenAI #Mistral Note- Curated using OpenAI models but prompts are mine

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