奥利弗Baudoux
Mitchell, An Enlyte 公司全球产品战略和人工智能高级副总裁
六十多年了, innovators have attempted to unlock the full potential of artificial intelligence (AI). 尽管一再试图推进它的使用, it wasn’t until the past decade that the science finally caught up to expectations. 如今，人工智能市场的预测有望实现 到2024年达到5000亿美元. 这种戏剧性的增长在过去一年中只是加速了, 在某种程度上, 致大流行和“新现实”. COVID-19是数字化转型的催化剂 快速跟踪人工智能的采用和接受. As insurers embrace AI and its ability to improve the claims process, they are 投入更多的技术预算 到人工智能亚博真人官方版APP. 事实上，根据一份报告， 87%的携带者 现在每年在这些技术上的花费是否超过500万美元, 这比银行业和零售业更重要吗.
超过六十年的制作
虽然人工智能的使用对汽车保险行业来说可能是新的, 这门科学已经存在了半个多世纪. 它诞生于1956年, the same year that President Eisenhower authorized construction of the interstate highway system. 我们永远不会知道人工智能是否先锋 约翰·麦卡锡 imagined a future where “the science and engineering of making intelligent machines” would enable vehicles to eventually drive themselves down those new highways. 然而, we do know that decades after McCarthy coined the term “artificial intelligence,“人工智能系统仍然难以实现曾经承诺的重大影响.
机器和深度学习的突破
在接下来的几十年里，人们对人工智能的兴趣持续增长. 这不是 直到20世纪80年代, 虽然, that scientists moved beyond hard-coded algorithms to 机器学习—a subset of AI that makes automation possible by generating predictions based on both data and learned experiences. 机器学习算法可以快速审查大量信息, 组织它, 提取关键数据并提出建议. 深度学习是科学的一个分支 机器学习 它的功能和人脑一样，很快就出现了. 到2012年，深度学习算法已经非常强大 谷歌街景，苹果Siri 以及其他流行的应用程序. As 麦肯锡 & 公司 指出的那样, it’s through machine and deep 学习 that AI can deliver on insurance industry expectations. “随着深度学习技术的新浪潮, 比如卷积神经网络, 人工智能有可能实现其模仿感知的承诺, 推理, 学习, 以及人类解决问题的能力. 在这个进化过程中, insurance will shift from its current state of ‘detect and repair’ to ‘predict and prevent’, 在这个过程中改变了行业的方方面面.”
释放人工智能的潜力
AI-enabled solutions have opened up new possibilities for auto insurers and collision repairers. 从发现车祸到 物联网技术, to instantly processing a payment for completed repairs, the opportunities are endless. 这是大多数运营商的首选, 然而, is using AI to automate the appraisal process and produce a “touchless” estimate. 这可以 提高效率，缩短周期，满足投保人期望 为了简化的数字索赔体验. 现在，由于这四个趋势，创造这种体验是触手可及的.
1. 转移检验方法
在COVID-19之前，虚拟估算保留给 低严重性索赔. 然而, the need for social distancing during the pandemic and changing consumer demands spurred the adoption of 虚拟检测方法. 2020年4月, 米切尔的数据显示，虚拟的使用, 或者摄影, 估计是今年早些时候的两倍多. 仅仅一年后， LexisNexis风险亚博真人官方版APP报告 that virtual claims handling has now “settled to a level of a little over 60%”. This shift in method of inspection opened the door to the long-term aspiration of “touchless” claims and leveraging AI in the appraisal process. 在过去的一年中，虚拟化被认为是 第一级自动化-提高了估计效率和一致性. 从图像，评估师可以完成大约 每天估计15到20次 而只有三到四个人在战场上. 这促使更多的运营商——据报道近70%的运营商 LexisNexis风险亚博真人官方版APP-开启理赔自动化之旅.
2. 大数据的盛行
根据 保险政策与研究中心, “The successes of AI are also being facilitated by the massive amounts of data we have today. 我们现在创造的数据的财富是惊人的, and the speed at which data is generated has only made data management tools like AI even more important.“财产险和意外险行业一直 善于捕捉、分析和解释数据. 无论是来自移动设备, 汽车物联网传感器或其他来源, this data gives decision makers the information necessary to personalize customer interactions and proactively address issues. 当涉及到非接触式估算时，仅靠数据是不够的. 获得全面的车辆库, repair and historical claims information is needed—along with the ability to quickly interpret that information using AI. 在…的情况下 米切尔智能估算，收集索赔细节和图像. 然后人工智能分析数据, comparing it to Mitchell’s comprehensive library of vehicle and repair information that spans more than 30 years. 从那里, the machine-学习 algorithms translate the output into component-level estimate lines for appraiser review and approval.
3. 人机协作
就像人类不断学习和进步一样，机器也是如此. 如下所示 保险思想领导力好的机器学习系统包含反馈循环...通过让机器知道“现实世界”发生了什么, 机器学习和改进”——和理赔员没什么不同! Support for a human-machine feedback loop is critical to automating the claims process and can lead to vast improvements in speed and accuracy. 估价师的反馈有助于教会机器做出更好的决策. 因为人工智能亚博真人官方版APP消除了可重复的任务, employees have more time to focus on complex claims that may require extra scrutiny.
4. 云计算和开放生态系统的发展
人工智能对数据的依赖 增加需求 for cloud-based systems—like Mitchell’s Program Freedom—that can access and aggregate vast amounts of information, 让它在任何地方都可以使用. 这些系统有帮助 组织减少了开发和维护成本, 增强安全性和可访问性, 提高速度, 可靠性和可伸缩性. Like cloud computing, open ecosystems are also vital to AI and touchless estimating. 开放的生态系统允许人工智能轻松访问数据, 跨平台和供应商的分析和软件, 给运营商创造凝聚力的能力, 端到端索赔经验. 据报道，它们还带来了灵活性和选择 PropertyCasualty360. “Choice in data providers that can collectively drive better and faster decisions, and the choice in technology partners that best aligns with an insurer’s claims experience, 产品线, 实践, 以及风险的观点.” Mitchell 智能开放平台 (MIOP) is a perfect example of how cloud-based solutions and open ecosystems are being used to automate the appraisal process. 通过MIOP，运营商可以选择最符合自身需求的人工智能. 这包括内部开发的人工智能算法， 米切尔提供 或通过第三方交付，如 易处理的 or 索赔的天才. 米切尔智能估算, the AI output is used to produce a partial or complete appraisal in Mitchell 云估计.
未来的人工智能索赔
By 2030, 麦肯锡 & 公司 predicts that more than half of current claims activities will be replaced by AI-enabled automation. “个人电话和小企业保险的索赔基本上是自动化的, enabling carriers to achieve straight-through processing rates of more than 90% and dramatically reducing claims processing times from days to hours or minutes.“现在科学已经准备好实现它在1950年的承诺, 汽车保险行业已经到了一个转折点. Carriers can either invest in AI or run the risk of being stranded on the side of the road. 最终, it will be those organizations that embrace this “new” technology to deliver a digitally driven claims experience that are best positioned to gain market share and consumer loyalty.