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Plateformes de données connectées
pour l’IoT des assurances et les analyses prédictives

cloud Les assurances dans le rapport Connected World

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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.

Des entreprises basées sur les données grâce aux applications analytiques avancées

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.

exemples d'utilisation

Établir une vision à 360° du client

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.


Dynamiser la productivité des agents grâce à un portail unifié

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.


Créer un cache à haut débit pour traiter les documents des dossiers

Dès qu'un client décide d'acheter une nouvelle police d'assurance, l'agent et/ou l'assureur doit encore traiter les documents du dossier concerné. Ce processus manuel peut être long et créer des erreurs. La rapidité d'exécution est importante, mais l'exactitude l'est tout autant. Un client d'Hortonworks du secteur des assurances a créé un cache pour documents basé sur HDP. Apache HBase met en cache les documents créés suite à une transaction, et attribue des méta-tags qui accélèrent le traitement. Et comme l'architecture d'HDP basée sur YARN prend en charge le traitement en multi-tenant du même ensemble de données, le suivi de document ne ralentit pas le processus d'évaluation du risque ou les autres analyses requises avant la mise en place de la couverture. Le traitement efficace des documents réduit les coûts et améliore la productivité des agents et des assureurs.


Détecter les fraudes

La fraude à l'assurance est un défi majeur du secteur. D'après le FBI, « le coût total de la fraude aux assurances (hors assurance maladie) est évalué à plus de 40 milliards de dollars par an. Cela signifie que la fraude aux assurances coûte entre 400 $ et 700 $ par an au foyer américain moyen sous la forme d'augmentation des primes. » Aux États-Unis, il existe plus de 7 000 compagnies d'assurance qui collectent plus d'un trillion de dollars de primes tous les ans. C'est donc une cible vaste et très lucrative pour les fraudeurs. Ceux-ci peuvent facilement masquer leurs traces alors qu'ils prennent part à des combines comme le détournement des primes, le gonflement des commissions, le détournement de biens ou la fraude aux indemnités d'accidents du travail. L'un des plus grands assureurs américains utilise HDP pour l'apprentissage automatique et la modélisation prédictive. Celle-ci applique des signaux basés sur des règles sur les données transmises, afin de détecter davantage de déclarations frauduleuses ou non valides. Lorsque les données sont envoyées dans le système, des alertes en temps réel aident les analystes spécialisés en déclarations à donner la priorité aux déclarations aux probabilités de fraude les plus élevées.

Lancer des services de réduction des risques

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.

Chiffrer le risque grâce aux données de capteurs empiriques

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.