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.
顧客が保険契約の新規購入に同意すると、代理店や査定人は申込書類を処理する必要があります。長期に渡りマニュアルで行なう処理が多いため、手続き不備のおそれがあります。速度は重要ですが、精度も同様に重要です。保険業界のある Hortonworks 契約者は、HDP 上に企業のドキュメントキャッシュを構築しました。Apache HBase は、処理を高速化するメタタグと共に、トランザクション後のドキュメンテーションをキャッシュ処理します。HDP の YARN ベースのアーキテクチャは同じデータセットでマルチテナント処理をサポートするため、文書をトラッキングしても、保証を開始する前に必要なリスク評価やその他の解析が遅延することはありません。効率的な文書処理は、コストを削減し、代理店と査定人の生産性を向上させます。
保険金詐欺は、保険業界の大きな課題です。FBI によると「保険金詐欺（健康保険以外）の総額は、年間以上 400 億ドルと推定される。これにより保険金詐欺は、米国の平均的な家庭に対して保険料増加という形で年間 400～700 ドルの負担を強いることを意味する。」とのことです。毎年保険料で 1 兆ドル以上を集める保険会社が 7,000 社以上あるため、犯罪者にとっては、大規模でもうかるターゲットになっています。保険料流用、手数料の過剰請求、資産流用、労災補償詐欺のような犯罪を犯す際に、簡単に自分の足跡を消すことができるのです。ある米国最大の保険会社は、機械学習や予測モデリングに HDP を使用しています。ルールに基づいたフラグをストリーミングデータに採用して、詐欺や無効請求をキャッチしています。請求データがシステムに流れ込むと、特別な調査や請求を担当するアナリストは、リアルタイムのアラートを頼りに、詐欺の可能性が高い請求の調査を優先します。
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.