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보험 IOT 및 예측 분석을 위한
연결 데이터 플랫폼

클라우드 연결된 세계의 보험 보고서

보고서 다운로드

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.

고급 분석 애플리케이션으로 데이터 중심적인 비즈니스 구축

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.

이용 사례

고객에 대한 전방위적 시각 구축

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.


통합 보험설계사 포털을 사용하여 보험설계사의 생산성 증대

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.


신청서 처리용 고속 캐시 만들기

고객이 새로운 보험을 구입하기로 합의한 후에도 보험설계사 및/또는 손해사정사에게는 신청서를 처리해야 하는 일이 남습니다. 이 일은 누출을 초래할 수 있는 장시간의 수동 프로세스가 될 수 있으며 일을 빨리 처리하는 것도 중요하지만, 정확성도 중요합니다. 보험 산업의 한 Hortonworks 고객은 HDP에 인터프라이즈 문서 캐시를 구축했습니다. Apache HBase는 거래 후 문서를 처리 속도를 높이는 메타 태그와 함께 캐시에 저장합니다. 그리고 HDP의 YARN 기반 아키텍처는 동일한 데이터 세트에 대해 멀티테넌트 처리를 지원하므로, 문서 추적으로 인해 리스트 평가나 기타 보증을 시작하기 전에 필요한 분석 작업의 속도가 느려지지 않습니다. 또한 문서가 효율적으로 처리되어 비용이 절감되고 보험설계사와 손해사정사의 생산성이 향상됩니다.


부정 행위 적발

보험 사기는 업계에서 직면하는 중요한 문제입니다. FBI에 따르면 "보험 사기로 인해 발생하는 총 비용(의료 보험 제외)은 연간 400억 달러 이상으로 추정되며, 이는 보험 사기로 인해 평균 미국 가정이 매년 400에서 700달러의 보험료를 추가로 부담해야 함을 의미한다"고 합니다. 해마다 총 1조 달러 이상의 보험료를 받는 7,000개가 넘는 보험 회사는 범죄자들에게 크고 수익성이 높은 표적이 되며 범죄자들은 보험료 유용, 수수료 교란, 자산 유용 또는 산재 보험 같은 수법을 감행한 흔적을 쉽게 숨길 수 있습니다. 미국 최대의 보험사 중 하나는 스트리밍 데이터에 규칙 기반 플래그를 적용하여 부정하거나 부당한 보험금 청구를 더 많이 적발하는 머신 러닝과 예측 모델링에 HDP를 사용합니다. 보험금 청구 데이터가 시스템에 유입될 때 실시간으로 발령되는 경보를 통해, 특별 조사 및 보험금 청구 분석가는 사기 가능성이 가장 높은 보험금 청구를 우선적으로 조사할 수 있습니다.

리스크 완화 서비스 시작

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.

실증적 센서 데이터를 사용한 리스크 가격 책정

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.

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