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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.
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.
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.
Uma vez que os clientes concordem em comprar uma nova apólice, o agente e/ou subscritor ainda precisam processar os documentos de candidatura. O processo manual pode ser moroso e causar inconsistências. A velocidade é importante, mas a precisão também é. Um cliente da Hortonworks no setor de seguros criou um cache de documentos corporativos na HDP. O Apache HBase agrupa a documentação pós-operação, com meta-tags que aceleram o processamento. E como a arquitetura da HDP, baseada em YARN, permite o processamento multi-tenant no mesmo conjunto de dados, o rastreamento de documentos não abranda a avaliação de risco ou outras análises necessárias antes de iniciar a cobertura. O processamento de documentos eficiente reduz os custos e aumenta a produtividade do agente e do subscritor.
A fraude de seguros é um grande desafio do setor. De acordo com o FBI, "Estima-se que o custo total de fraudes no setor de seguros (exceto seguro de vida) chegue a US $ 40 bilhões por ano. Isso significa que as fraudes no setor de seguros custe a uma família norte-americana de classe média entre US$ 400,00 e US$ 700,00 por ano na forma de aumento dos prêmios". Como há mais de 7.000 companhias de seguros que recolhem mais de US$ 1 trilhão em prêmios todos os anos, os criminosos têm um alvo grande e lucrativo. Eles podem facilmente esconder seus rastros, pois cometem esquemas como desvio de prêmios, churning de taxas, desvio de ativos ou fraude de compensação de trabalhadores. Uma das maiores seguradoras dos Estados Unidos usa a HDP para aprendizagem automática e modelagem preditiva que emprega bandeiras baseadas em regras sobre fluxo de dados, com o intuito de deter maior quantidade de sinistros fraudulentos ou inválidos. Conforme os dados de sinistros chegam ao sistema, alertas em tempo real ajudam na investigação especial; os analistas de sinistros priorizam suas investigações sobre os sinistros que apresentam maior probabilidade de fraude.
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.
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.