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Connected Data Platforms for
Insurance IOT and Predictive Analytics

nube Informe sobre los seguros en el mundo conectado

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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.

Construcción de empresas centradas en los datos con aplicaciones analíticas avanzadas

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.

Use Cases

Build a 360° View of the Customer

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.

Impulsa la productividad del agente con un portal unificado

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.

Crea un caché de alta velocidad para el procesamiento de documentos de solicitud

Una vez que los clientes están de acuerdo en comprar un nuevo seguro, el agente y/o asegurador todavía tiene que procesar los documentos de solicitud. Esto puede ser un proceso manual largo que provoca errores. La velocidad es importante, pero también lo es la precisión. Un abonado a Hortonworks en la industria de los seguros construyó un caché de documentos en HDP. Apache HBasecaches, la documentación posterior a la transacción, con meta-etiquetas que aceleran el procesamiento. Y debido a que la arquitectura de HDP basada en YARN soporta el procesamiento del mismo conjunto de datos por múltiples usuarios, el seguimiento de la documentación no ralentiza la evaluación de riesgos u otras analíticas necesarias para iniciar la cobertura. El procesamiento de datos eficiente reduce los costes y mejora la productividad del agente y del suscriptor.

Detecta el fraude

El fraude al seguro es un reto importante en la industria. Según el FBI, "El coste total del fraude al seguro (no en seguros de saludo) se estima en más de 40 mil millones de dólares al año. Esto significa que el fraude cuesta a la familia estadounidense media de 400 a 700 dólares al año en forma de aumento de primas". Debido a que hay más de 7.000 compañías de seguros que recolectan más de 1 billón de dólares en primas al año, los criminales tienen un objetivo grande y lucrativo. Puede ocultar fácilmente sus pistas mientras perpetúan sus crímenes desviando las primas. Una de las compañías de seguros más grandes de los Estados Unidos utiliza HDP para el aprendizaje automático y el modelado predictivo que emplea banderas basadas en reglas de transmisión de datos para atrapar al fraude o las reclamaciones no válidas. Como los datos de las reclamaciones fluyen en el sistema, las alertas en tiempo real ayudan a la investigación para analizar y priorizar las reclamaciones con mayor probabilidad de fraude.

Presenta servicios de reducción de riesgos

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.

Valora el riesgo con datos empíricos

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.