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Connected Data Platforms for
Insurance IOT and Predictive Analytics

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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.

Creazione di attività incentrate sui dati con le applicazioni analitiche avanzate

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.


Rilanciare la produttività degli agenti con un portale unico a loro dedicato

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.


Creazione di una cache ad alta velocità per l'elaborazione delle domande di assicurazione

Una volta che il cliente ha accettato l'acquisto di una nuova polizza, l'agente e/o l'assicuratore devono elaborare i documenti relativi alla richiesta. Queste operazioni possono rappresentare una procedura manuale lunga in cui si possono verificare perdite di dati. La velocità è importante, ma lo è altrettanto l'accuratezza. Un cliente di Hortonworks nel settore assicurativo ha creato una cache per i documenti aziendali su HDP. Apache HBase trasferisce nella cache i documenti creati in seguito alla transazione, con meta-tag per velocizzare l'elaborazione. Inoltre, poiché l'architettura basata su YARN di HDP supporta l'elaborazione multi-tenant sul medesimo set di dati, tracciare un documento non rallenta la valutazione del rischio né altre analisi necessarie prima dell'attivazione della copertura. L'elaborazione efficace dei documenti riduce i costi e migliora la produttività di agente e assicuratore.


Rilevamento delle frodi

Le frodi assicurative costituiscono un problema rilevante nel settore. Secondo l'FBI, "Il costo totale stimato delle frodi assicurative (escluse quelle sanitarie) supera i 40 miliardi di dollari all'anno. Ciò significa che le frodi assicurative costano alla famiglia media americana fra i 400 e i 700 dollari l'anno, traducendosi in aumenti dei premi". Con oltre 7000 compagnie di assicurazione che riscuotono oltre mille miliardi di premi ogni anno, i truffatori hanno a disposizione un bacino ampio e remunerativo. Con la possibilità di celare facilmente i dati che li riguardano, seguono ripetutamente schemi di appropriazione indebita dei premi o dei beni dell'assicurazione, fee churning, o truffe sulle assicurazioni per dipendenti. Una delle maggiori compagnie assicurative americane utilizza HDP per l'apprendimento automatico e la definizione di modelli predittivi che utilizzano regole per generare dei flag sui dati di streaming al fine di individuare un numero maggiore di richieste di indennizzo fraudolente o non valide. Grazie ad avvisi in tempo reale all'ingresso dei dati delle richieste di indennizzo, è possibile svolgere su di esse indagini speciali o analisi, seguendo un ordine di priorità in base al rischio di frode che presentano.

Lancio di servizi di riduzione del rischio

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.

Rischio di prezzo con dati empirici ricavati da sensori

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.