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面向保险机构物联网和预测分析的
互联数据平台

云 “互联世界中的保险”报告

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Beat risk

With Hortonworks connected data platforms for insurance IOT, much more is possible. For example, a 360° view of not only your customers but also connected cars, helps you understand where and how they are driving while providing better predictive analytics from all the customer big data in the insurance industry.  You can now provide them with recommendations for alternative safer routes and driving behavior making them better drivers.

通过高级分析应用程序构建以数据为中心的业务

Changes in technology and customer expectations create new challenges for how insurers engage their customers, manage risk information and control the rising frequency and severity of claims. Carriers, like Progressive, are tapping Hortonworks for insurance IOT and predictive analytics to help rethink traditional models for customer engagement.

使用案例

构建客户的全方位概览

Carriers interact with customers across multiple channels, yet customer interaction, policy and claims data is often isolated in data silos. Few insurance carriers can accurately correlate acquisition, cross-sell or upsell success with either their marketing campaigns or customer online browsing behavior. Collecting and managing data from insurance IOT devices, Apache Hadoop gives the insurance enterprise a 360° view of customer behavior. It lets them store data longer and identify distinct phases in their customers’ lifecycles. Better insurance predictive analytics helps them more efficiently acquire, grow and retain the best customers.


通过统一代理商门户来提升代理商生产力

Many carriers sell policies through agents. To prepare for sales calls (or to answer questions from prospects during those calls) those agents may need to look up details across multiple, disjointed platforms or applications. This takes time and decreases sales velocity. Unlike legacy data platforms, HDP stores data from many sources including insurance IOT, in a “data lake”. This permits a single lookup, without requiring multiple individual queries across different unrelated storage platforms. Agents prepare themselves more thoroughly, and they can make more calls over a given time period, helping grow revenue. Insurance companies can also use the same type of single view to understand which agents are most productive selling their products—offering incentives that promote top performers or de-certifying the chronically unproductive.


创建高速缓存以处理申请文档

一旦客户同意购买新保单,代理商和/或保险商仍需要处理申请文档。这可能是漫长的人工过程,可能导致信息泄露。速度很重要,但精确性同样重要。保险行业的一家 Hortonworks 订户在 HDP 上构建了企业文档缓存。Apache HBase 将交易后文档进行缓存,同时包含元标签以加速处理。并且由于 HDP 的基于 YARN 的架构支持多租户处理同一个数据集,所以文档跟踪不会减慢风险评估或者在启动保险项目之前所需要的其他分析。高效率的文档处理不仅降低成本,还可提高代理商和保险商的生产力。


检测欺诈

保险欺诈是行业内的重大难题。根据 FBI 的调查,“保险欺诈(不包括健康保险)所导致的总成本估计每年超过 400 亿美元。这意味着,保险欺诈每年平均让每个美国家庭以增加保险费的形式花费 400 到 700 美元”。因为有 7,000 多家保险公司每年收入超过 1 千亿美元的保险费,因此犯罪具有庞大且利润丰厚的目标。他们可以在实施保险费转移、费用流动、资产转移或工人补偿欺诈等犯罪时轻松隐藏自己。美国最大的保险公司之一利用 HDP 进行机器学习和预测建模,对流数据采用基于规则的标记,以捕获更多欺诈性索赔或无效索赔。在索赔数据进入系统时,实时警报将帮助特别调查和索赔分析师以最高欺诈可能性来划分其索赔调查的优先级。

启动降低风险服务

Insurance companies understand risk and—as in other industries—they are moving from reactive to proactive uses of their data. Any claims adjuster has seen accidents, fires or injuries that could’ve been foreseen and maybe prevented, drawing conclusions like: “He shouldn’t have been out driving in that weather,” or “Those wires were long past their replacement age.” Now with insurance predictive analytics, insurers are capturing and sharing that insight with their customers before the losses occur. With these risk-reduction and prevention services, carriers share real-time analytics with policyholders, so they can prevent mishaps. For example, they can establish algorithms to identify emerging high-risk phenomena having to do with foul weather, disease epidemics, or equipment recalls—and provide timely alerts that help their customers protect themselves and their property. One Hortonworks customer that offers car insurance is working on real-time alerts that will notify drivers when a strong storm will affect a particular stretch of road and then also suggest less-risky alternate routes.

借助实证传感器数据来为风险定价

Moral hazard describes the phenomena of one person taking more risk because someone else bares the burden of that risk. When a company offers an auto insurance policy, they face moral hazard because of information asymmetry—policyholders know more about how they actually drive than does the carrier. Drivers may drive a bit faster or watch the road a little less closely because they know that they are covered in the event of a collision. Carriers set prices to cover that moral hazard, and so the safer drivers end up subsidizing those who take more risks on the road. Usage-based insurance (UBI) has the potential to reduce information asymmetry and moral hazard by rewarding safe drivers for their good behavior. A major insurer runs its UBI products with insurance iot and telematic sensor data stored in HDP. Prior non-Hadoop processing captured only a subset of UBI data streaming from sensors in policyholders’ cars and extract-transform-load (ETL) processes delayed availability of that data until the week after capture. With HDP, the company captures and stores all driving data from customers that opt in to UBI, processes the larger dataset in half the time, and uses predictive modeling to reward those drivers for how they actually drive rather than guessing on how they might drive based only on their age, type of car, location and prior history.