人工智能及其对汽车索赔的影响

六十多年了, 创新者试图释放人工智能(AI)的全部潜力. Despite repeated attempts to advance its use, it wasn’t until the past decade that the science finally caught up to expectations. Today, AI market projections are on track to reach $500 billion by 2024. That dramatic growth has only accelerated over the last year due, 在某种程度上, 致大流行和“新现实”. Serving as a catalyst for digital transformation, COVID-19 has 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 devoting a larger portion of their technology budgets 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 are now spending in excess of $5 million annually on these technologies, which is more than in the banking and retail sectors.

超过六十年的制作

While the use of AI may be new to the auto insurance industry, the science has been around for over a half-century. 它诞生于1956年, 同年，艾森豪威尔总统批准了州际高速公路系统的建设. 我们永远不会知道人工智能是否先锋 约翰·麦卡锡 想象一下这样一个未来:“制造智能机器的科学和工程”将使车辆最终能够在这些新的高速公路上自动驾驶. 然而, we do know that decades after McCarthy coined the term “artificial intelligence,” AI systems still struggled to deliver the significant impact once promised.

Machine- and Deep-Learning Breakthroughs

Over the next several decades, interest in AI continued to grow. 这不是 直到20世纪80年代, 虽然, 科学家们从硬编码算法转向了机器学习——这是人工智能的一个子集，通过基于数据和学习经验生成预测，使自动化成为可能. Machine-学习 algorithms can quickly review vast amounts of information, 组织它, extract key data and make recommendations. 深度学习是机器学习的一个分支，其功能类似于人类的大脑. By 2012, deep-学习 algorithms were powering 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, 只有通过机器和深度学习，人工智能才能满足保险业的期望. “With the new wave of deep 学习 techniques, 比如卷积神经网络, AI has the potential to live up to its promise of mimicking the perception, 推理, 学习, 以及人类解决问题的能力. 在这个进化过程中, 保险将从目前的“检测和修复”转变为“预测和预防”。, transforming every aspect of the industry in the process.”

释放人工智能的潜力

人工智能亚博真人官方版APP为汽车亚博真人官方版APP和碰撞维修公司开辟了新的可能性. 从发现车祸到 IoT技术，以即时处理付款完成维修，机会是无穷无尽的. 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 improve efficiency, shorten cycle time and meet policyholder expectations for a streamlined, digital claims experience. Now, thanks to these four trends, creating that experience is within reach.

1. 转移检验方法

Prior to COVID-19, virtual estimating was reserved for low severity claims. 然而, 大流行期间保持社交距离的需要和不断变化的消费者需求促使采用了 虚拟检测方法. 2020年4月, Mitchell data shows that the use of virtual, 或者摄影, estimating more than doubled from earlier in the year. 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%”. 这种检查方法的转变为“非接触式”索赔的长期愿望打开了大门，并在评估过程中利用人工智能. Over the last year, virtualization—considered the 第一级自动化—has resulted in estimate efficiency and consistency gains. From images, appraisers can complete approximately 每天估计15到20次 而只有三到四个人在战场上. This has prompted more carriers—nearly 70 percent according to LexisNexis风险亚博真人官方版APP—to embark on the claims automation journey.

2. 大数据的盛行

根据 Center for 保险 Policy and Research“人工智能的成功也得益于我们今天拥有的大量数据. The wealth of data we now create is astonishing, 数据生成的速度只会让像人工智能这样的数据管理工具变得更加重要.” The property and casualty industry has always thrived on capturing, 分析和解释数据. 无论是来自移动设备, automobile IoT sensors or other sources, 这些数据为决策者提供了个性化客户互动和主动解决问题所需的信息. When it comes to touchless estimating, 虽然, data alone isn’t enough. Access to a comprehensive library of vehicle, 需要维修和历史索赔信息，以及使用人工智能快速解释这些信息的能力. 在…的情况下 米切尔智能估算, claim details and images are collected. 然后人工智能分析数据, 将其与米切尔长达30多年的车辆和维修信息综合图书馆进行比较. 从那里, 机器学习算法将输出转换为组件级估计线，供评估师审查和批准.

3. 人机协作

Just as humans continually learn and improve, so do machines. 如下所示 保险思想领导力, “good machine 学习 systems involve feedback loops...By letting the machine know what happens on the ‘real world’ side of things, machines learn and improve"—no different from claims adjusters! 支持人机反馈循环对于自动化索赔流程至关重要，并且可以大大提高速度和准确性. An appraiser’s feedback helps teach the machine to make better decisions. As AI-powered solutions remove repeatable tasks, employees have more time to focus on complex claims that may require extra scrutiny.

4. The Growth of Cloud Computing and Open Ecosystems

人工智能对数据的依赖 增加需求 基于云的系统，比如米切尔的自由程序，可以访问和汇总大量的信息, 让它在任何地方都可以使用. These systems help organizations reduce development and maintenance costs, 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. Open ecosystems allow AI to easily access data, analytics and software across platforms and providers, giving carriers the ability to create a cohesive, 端到端索赔经验. They also introduce flexibility and choice, reported PropertyCasualty360. “Choice in data providers that can collectively drive better and faster decisions, 选择最符合亚博真人官方版APP理赔经验的技术合作伙伴, 产品线, 实践, 以及风险的观点.“米切尔智能开放平台(MIOP)是云亚博真人官方版APP和开放生态系统如何用于自动化评估过程的一个完美例子. Through the MIOP, carriers can select the AI that best meets their needs. That includes AI algorithms developed internally, 米切尔提供 or delivered through third parties such as 易处理的 or 索赔的天才. 米切尔智能估算, 在米切尔云估计中，人工智能输出用于产生部分或完整的评估.

未来的人工智能索赔

By 2030, 麦肯锡 & 公司 预测目前超过一半的理赔活动将被人工智能自动化所取代. “Claims for personal lines and small-business insurance are largely automated, 使运营商能够实现90%以上的直通式处理率，并将索赔处理时间从几天大幅缩短到几小时或几分钟.” With the science now ready to deliver on its 1950’s promises, the auto insurance industry has reached a turning point. 运营商要么投资人工智能，要么冒着被困在路边的风险. 最终, 只有那些采用这种“新”技术来提供数字驱动的索赔体验的组织，才最有可能获得市场份额和消费者忠诚度.