Ancileo

💡 💡 𝗔𝗻𝗰𝗶𝗹𝗲𝗼 𝗚𝗲𝗻 𝗔𝗜 : 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 At Ancileo, we’ve shared our journey of building a scalable GenAI module for travel insurance claims. Tackling challenges like handling unstructured data and managing demand surges has shaped our approach to creating reliable, efficient systems. 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗙𝗼𝗰𝘂𝘀: 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟲 – 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: 1️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗳𝗼𝗿 𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝘂𝗿𝗴𝗲𝘀 ⚙️ Real-time scaling using Kubernetes adjusts resources to match claim volumes during events like flight cancellations or natural disasters. 2️⃣ 𝗖𝗼𝘀𝘁-𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 📊 Quantized AI models reduce computational costs without compromising accuracy, enhancing efficiency during high-volume periods. 3️⃣ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 ☁️ A hybrid cloud system balances cost and scalability by combining in-house servers for routine claims with cloud resources for peak demand. 🎯 𝗛𝗼𝘄 𝗪𝗲 𝗦𝗼𝗹𝘃𝗲𝗱 𝗜𝘁 👇 🔹 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 Automatically adjusted resources to handle sudden spikes, such as those caused by natural disasters, while scaling down during low-demand periods to minimize costs. 🔹 𝗧𝗮𝘀𝗸-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗣𝗼𝗱𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Dedicated pods handle specific tasks like fraud detection or document verification, ensuring resources are allocated optimally for different claims. 🔹 𝗕𝗶𝗻𝗮𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗼𝘀𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 By using quantized AI models with binary encoding, the system reduces computational loads during peak periods, balancing speed and cost. 🔹 𝗛𝘆𝗯𝗿𝗶𝗱 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝘁𝘂𝗽 Enabled seamless transitions between in-house servers and cloud during demand surges. 📍 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✅ 𝟯𝟬% 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 in processing costs during peak demand. ✅ 𝟯𝘅 𝗳𝗮𝘀𝘁𝗲𝗿 claim assessments, ensuring timely resolutions. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 to handle fluctuating volumes reliably. Balancing cost, speed, and scalability, these solutions equip insurers with the tools needed to manage claims effectively, especially in high-demand situations. 👉 Read the full article here: https://lnkd.in/g6JK_8ax #TravelInsurance #ClaimsProcessing #DynamicScaling #CostOptimization #CloudInfrastructure #AI #InsurTech #Ancileo #Lea

🔍 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗢𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗖𝗹𝗮𝗶𝗺𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 As part of developing Lea’s AI eligibility module, we tackled multiple challenges and learned valuable lessons along the way. Handling travel insurance claims during disruptions requires scalable and efficient systems. Using 𝗰𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼-𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀, we built a solution to manage high claim volumes effectively. 🔍 𝗧𝗼𝗱𝗮𝘆'𝘀 𝗙𝗼𝗰𝘂𝘀 - 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟱: 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗟𝗲𝗮𝗿𝗻𝗲𝗱: 1️⃣ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 🔍 Flags high-risk claims by analyzing location, cost, and claimant history, ensuring efficient resource allocation. 2️⃣ 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 ⚠️ Layered workflows route issues like missing data or fraud indicators to the right team for faster resolution. 3️⃣ 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆 🔗 Fallback workflows and human-in-the-loop processes keep claims moving during AI downtimes. 🎯 𝗛𝗼𝘄 𝗪𝗲 𝗦𝗼𝗹𝘃𝗲𝗱 𝗜𝘁: 🔹 𝗥𝗶𝘀𝗸-𝗕𝗮𝘀𝗲𝗱 𝗥𝗼𝘂𝘁𝗶𝗻𝗴: Flags high-cost or high-risk claims for priority review. 🔹 𝗘𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀: Time-based rules ensure delays are minimized. 🔹 𝗔𝗜 + 𝗛𝘂𝗺𝗮𝗻 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽: Continuous improvement reduces false positives and optimizes accuracy. 🎉 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✅ Streamlined error management ✅ Uninterrupted claims processing ✅ Improved accuracy and speed Every challenge we faced while developing this system taught us something new. From addressing high-risk claims to ensuring operational continuity, each solution brought us closer to creating a reliable, enterprise-ready platform for travel insurers. 👉 Read the full article here: https://lnkd.in/geZSQ4hT #TravelInsurance #ClaimsProcessing #RiskAssessment #ErrorHandling #AI #InsurTech #Ancileo #Lea 

🔄 𝗥𝗲𝗰𝗮𝗽: 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝗻 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 At Ancileo, we’ve shared insights into how our AI-driven fraud detection and cloud scaling are transforming travel insurance claims. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗿𝗲𝗰𝗮𝗽: 1️⃣ 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: How AI and clustering reduce fraud by identifying anomalies and patterns in claims data. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Using Kubernetes and task management tools to process high claim volumes efficiently. 👉 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀: 𝗥𝗲𝗮𝗱 𝗮𝗯𝗼𝘂𝘁 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 : https://lnkd.in/g_W7_TUi 𝗥𝗲𝗮𝗱 𝗮𝗯𝗼𝘂𝘁 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 : https://lnkd.in/gXxwQWSv 👉 Check out our detailed insights and let us know your thoughts!

🔄 𝗥𝗲𝗰𝗮𝗽: 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝗻 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 At Ancileo, we’ve shared insights into how our AI-driven fraud detection and cloud scaling are transforming travel insurance claims. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗿𝗲𝗰𝗮𝗽: 1️⃣ 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: How AI and clustering reduce fraud by identifying anomalies and patterns in claims data. 2️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Using Kubernetes and task management tools to process high claim volumes efficiently. 👉 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀: 𝗥𝗲𝗮𝗱 𝗮𝗯𝗼𝘂𝘁 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 : https://lnkd.in/g_W7_TUi 𝗥𝗲𝗮𝗱 𝗮𝗯𝗼𝘂𝘁 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 : https://lnkd.in/gXxwQWSv 👉 Check out our detailed insights and let us know your thoughts!

As part of developing Lea’s AI eligibility module, we faced multiple challenges. In this series, we share the obstacles, insights, and solutions. Managing travel insurance claims during disruptions requires scalable, efficient systems. By leveraging cloud infrastructure, Kubernetes, and auto-scaling policies, we built a system to handle high claim volumes effectively. 🔍 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗳𝗼𝗰𝘂𝘀 - 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟰: 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀 𝘄𝗶𝘁𝗵 𝗖𝗹𝗼𝘂𝗱-𝗕𝗮𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘄𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝗱 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: 1️⃣ 𝗖𝗹𝗼𝘂𝗱-𝗕𝗮𝘀𝗲𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Scales resources dynamically during demand surges to ensure uninterrupted processing. 2️⃣ 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Manages containerized applications, deploying and retiring containers based on workload. 3️⃣ 𝗔𝘂𝘁𝗼-𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗳𝗼𝗿 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Monitors system performance and adjusts resources automatically to meet real-time needs. 🎯 𝗛𝗼𝘄 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝘀: 𝟭️⃣  𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Kubernetes deploys additional containers during high demand (e.g., post-flight delays) and scales down during low traffic to optimize costs.   𝟮️⃣  𝗔𝘂𝘁𝗼-𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀: Pre-defined thresholds monitor CPU, memory, and workload to trigger scaling events in real time. Ensures system performance aligns with demand without manual intervention. 𝟯️⃣  𝗖𝗹𝗼𝘂𝗱 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: The system leverages cloud resources to add or remove capacity as needed, keeping operations efficient and responsive. 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: ✅ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Resources dynamically adjusted to match demand. ✅ 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Reduced expenses by decommissioning unused resources during low traffic. ✅ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: High-priority claims processed without delays, enhancing customer experience. Efficient scaling and task management ensure consistent performance, even during demand peaks. 📖 Read more about our cloud-powered approach here: https://lnkd.in/gXxwQWSv #TravelInsurance #InsuranceTechnology #InsurTech #ClaimsProcessing #CloudComputing #DynamicScaling #Kubernetes #AutoScaling #TaskManagement #AI #ArtificalIntelligence #Lea #Ancileo

💪 💪 💪 Ancileo 𝗚𝗲𝗻 𝗔𝗜 𝘃𝘀. 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀 As part of our journey in developing Lea’s AI eligibility assessment module, we’ve faced and addressed multiple challenges. In this series, we’re sharing the obstacles we encountered, the insights we gained, and the solutions we applied. 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗳𝗼𝗰𝘂𝘀 - 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟯: 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: 1️⃣ 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: AI handles diverse claims data effectively, ensuring accuracy in fraud detection. 2️⃣ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Machine learning flags unusual patterns (e.g., regional claim spikes), reducing manual intervention. 3️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗠𝗼𝗱𝗲𝗹 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: Daily retraining helps adapt to new fraud tactics and evolving data patterns. 🎯 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗗𝗶𝗱: 👇 1. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 🤔 Flags anomalies like unexpected values or mismatched details in claims, ensuring early detection. 2. 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 🔍 Identifies irregular patterns, such as frequent claims by the same policyholder or spikes in specific claim types. 3. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗧𝗿𝗲𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 🔗 Groups claims with similar characteristics to uncover trends and flag outliers for deeper review. 4. 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 📊 Updates models daily, ensuring detection systems stay relevant and effective against evolving fraud patterns. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: 📍 A 35% spike in baggage loss claims from one airport? Flagged for review. 📍 Unusually high medical expenses in a low-risk region? Investigated further. 📍 Frequent claims by a single policyholder? Marked as potential fraud. 🎉 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀? ✅ 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗙𝗿𝗮𝘂𝗱: Early detection of anomalies minimized financial impact. ✅ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Real-time validation improved decision-making efficiency. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Automated processes handled high claim volumes seamlessly. Fraud detection remains a critical focus as we continue to improve the efficiency and reliability of travel insurance claims processing. 📖 Follow our journey and read the full article here: https://lnkd.in/g_W7_TUi #TravelInsurance #ClaimsManagement #FraudDetection #Ancileo #Lea #Insurtech #Insurance #GenAI #AI #ArtificialIntelligence

🔄 𝗥𝗲𝗰𝗮𝗽: 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗚𝗲𝗻 𝗔𝗜 𝘃𝘀. 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗖𝗹𝗮𝗶𝗺𝘀  At Ancileo, we’ve been sharing insights into the development of our #travelinsurance AI claims eligibility module, which has been in production for 5 months. Building this system has been a learning process, and we’ve encountered and addressed some major challenges. 𝗛𝗲𝗿𝗲’𝘀 𝗮 𝘀𝘂𝗺𝗺𝗮𝗿𝘆 𝗼𝗳 𝘁𝗵𝗲 𝘁𝘄𝗼 𝘄𝗲’𝘃𝗲 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝘀𝗼 𝗳𝗮𝗿: 1️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗖𝗹𝗮𝗶𝗺𝘀 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 📂 Travel insurance claims involve a variety of formats, from scanned receipts and handwritten notes to structured digital data. 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗗𝗶𝗱: 👉 𝗦𝗺𝗮𝗿𝘁 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Categorized documents into types (e.g., receipts, medical certificates) to ensure only relevant data is processed. 👉 𝗗𝘂𝗮𝗹-𝗟𝗮𝘆𝗲𝗿 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Introduced a system to flag inconsistencies like mismatched currencies or missing fields for review. 👉 𝗘𝘃𝗲𝗻𝘁 𝗠𝗮𝗽𝗽𝗶𝗻𝗴: Mapped claims data (e.g., flight delays, baggage loss) to specific events, reducing manual intervention and accelerating decisions. 📖 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗽𝗼𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: [https://lnkd.in/geGiY_gH] 2️⃣ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗦𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ⏱️ Processing claims accurately and quickly requires real-time updates to claim histories, policy changes, and customer interactions. 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗗𝗶𝗱: 👉 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Created a synchronized replica of claim data to ensure updates across systems without delays. 👉 𝗖𝗼𝗻𝗳𝗹𝗶𝗰𝘁 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Developed protocols to manage out-of-order updates and inconsistencies during high-traffic periods like travel disruptions. 👉 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Reduced manual effort by automating repetitive tasks such as data validation and claims routing. 📖 𝗖𝗮𝘁𝗰𝗵 𝘂𝗽 𝗼𝗻 𝗼𝘂𝗿 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝘀𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: [https://lnkd.in/gHZcYZhH] 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗦𝗼 𝗙𝗮𝗿: ✅ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Claims involving unstructured data are now processed more efficiently with minimal manual intervention. ✅ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Real-time validation ensures data consistency across systems, reducing errors. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Automated workflows allow the system to handle increasing claim volumes without adding complexity. 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? We’ll share how we’re improving accuracy and security in claims assessment. 👉 Explore the details in our posts and stay tuned for updates! #TravelInsurance #InsurTech #ClaimsProcessing #ArtificialIntelligence #DataSynchronization #RealTimeData #Ancileo

𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝗣𝗜 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: 𝗛𝗼𝘄 𝗖𝗗𝗖 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝘀 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Travel insurers face challenges when managing claims and customer engagement workflows, especially during high-demand periods. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗶𝘀𝘀𝘂𝗲𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: 🚫 API rate limits restricting access to real-time data for claims and policy updates. 🕒 Delays in processing caused by manual interventions or outdated workflows. ⚠️ System strain from repeated API queries, impacting live database performance. These challenges can lead to frustrated customers, operational inefficiencies, and delays in critical updates. 🔧 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗮𝘁𝗮 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 (𝗖𝗗𝗖) We implemented CDC to provide real-time, scalable, and independent access to critical data—without overloading client systems or relying on API availability. 𝗛𝗼𝘄 𝗖𝗗𝗖 𝗪𝗼𝗿𝗸𝘀: 📡 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: CDC passively captures updates (e.g., claim statuses, policy changes) from client transaction logs and synchronizes them to a replica in real time. This ensures access to up-to-date information without querying the client’s live systems. ⚙️ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Event-based triggers activate workflows, such as sending claim updates, policy renewal notifications, or coverage confirmations. These workflows run seamlessly using replicated data, reducing the need for API calls. 🔒 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: CDC minimizes system intrusion by eliminating repeated queries, maintaining database performance even during claim surges or high-traffic periods. 🏆 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗲𝗿𝘀 🌐 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗲: Access real-time data without relying on API availability, ensuring continuity during high-demand periods. 🤝 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗧𝗿𝘂𝘀𝘁: Deliver timely, accurate updates, improving customer loyalty and retention. 📈 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Handle large volumes of claims and engagement tasks without impacting live systems or performance. Learn how CDC supports scalable, real-time engagement workflows for travel insurers: 👉 https://lnkd.in/gq5dK2Jn #TravelInsurance #ClaimsManagement #ChangeDataCapture #RealTimeData #Ancileo #Lea #claimhistory

Handling claims quickly and accurately is critical for travel insurers, yet unstructured data and manual processes often slow things down. Here’s how 𝗟𝗲𝗮’𝘀 𝗔𝗜 𝗘𝗹𝗶𝗴𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗠𝗼𝗱𝘂𝗹𝗲 addresses these challenges: 📂 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Lea’s AI automatically categorizes unstructured documents (e.g., receipts, medical bills, boarding passes) into predefined types, ensuring only relevant data is processed. This reduces classification errors and increases operational efficiency. 🔍 𝗗𝘂𝗮𝗹-𝗟𝗮𝘆𝗲𝗿 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: A two-step protocol identifies anomalies, such as unexpected currencies or missing details, ensuring claims meet accuracy standards before approval. ⏱️ 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗘𝘃𝗲𝗻𝘁 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱: Data is mapped to specific events (e.g., flight delays, stolen luggage), enabling faster claims decisions and reducing manual intervention. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗧𝗿𝗮𝘃𝗲𝗹 𝗜𝗻𝘀𝘂𝗿𝗲𝗿𝘀: ⏩ Faster Claims Processing: Automates classification and mapping, reducing processing times by up to 30%. ✅ 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Flags errors early, minimizing rework and ensuring consistency. 📈 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Supports large claim volumes without increasing team workloads. 📖 Missed last week’s post? Catch up here.👇👇 👉 Discover how Lea’s AI module is redefining travel insurance workflows—learn about the challenges, insights, and technical solutions behind this enterprise-ready system: 👉 https://lnkd.in/geGiY_gH

OTAs struggle to balance 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻, 𝗿𝗲𝘃𝗲𝗻𝘂𝗲, 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝘀𝘁𝘀 𝗳𝗼𝗿 𝘁𝗿𝗮𝘃𝗲𝗹 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲. But not all strategies deliver the same results. Here’s how (𝙘𝙝𝙚𝙘𝙠 𝙞𝙢𝙖𝙜𝙚) Global Partners, Regional Partners, and In-Path Insurance Integration compare. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗧𝗲𝗹𝗹𝘀 𝗨𝘀: • In-Path Insurance Integration achieves the 𝗵𝗶𝗴𝗵𝗲𝘀𝘁 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲 (8%) and revenue potential ($48M). • It delivers 𝘂𝗻𝗺𝗮𝘁𝗰𝗵𝗲𝗱 𝗥𝗢𝗜 (192x), outperforming other strategies. • Regional and global models are effective but 𝗹𝗮𝗴 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝗻 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁. 📊 These insights come from 𝗼𝘂𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝟯𝟬 𝗢𝗧𝗔𝘀, including adoption trends, revenue benchmarks, and case studies from leaders like Expedia Group, Traveloka, lastminute.com, Agoda and many others. 🚨 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗧𝗶𝗺𝗲 𝗢𝗳𝗳𝗲𝗿 🚨 📅 𝗙𝗿𝗲𝗲 𝗳𝗼𝗿 𝗢𝗧𝗔𝘀 𝘂𝗻𝘁𝗶𝗹 𝗗𝗲𝗰 𝟮𝟬𝘁𝗵. 𝗢𝘁𝗵𝗲𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 : $𝟵𝟵𝟳 👉 Download the full report here to see how your strategy compares: https://lnkd.in/gC7JGd5S 💬 What’s your approach to travel insurance? Let’s discuss below. #OTA #TravelInsurance #AncillaryRevenue #OTAStrategies #TravelIndustry #TravelTech #DigitalTravel #Ancileo #InsurTech #Ancileo