Optimizing IoT – Key Strategies for Commercial Insurers– Cognizant
Article Synopsis :
By aligning business requirements with IoT capabilities commercial insurers can sharpen operational efficiencies, open new revenue streams, drive profitable growth and enhance customer intimacy.
“Optimizing the Internet of Things” from Cognizant suggests ways commercial carriers can capitalize on IoT, with an eye to related business/technology challenges and potential strategies for overcoming them.
The main theme of the report is that commercial insurers in particular should proactively (vs. reactively) explore IoT technologies to improve existing business operations. Since this can’t be done all at once, a phased approach is recommended:
- Smart risk assessment: create proof-of-concepts in new technology arenas such as sensors and Blockchain.
- Smart risk detection and claims processing: create an ecosystem enabling real-time data collection from smart products.
- Risk prevention/mitigation: build a third-party partner ecosystem.
- New business models: re-think existing models with a new emphasis on data analytics.
The below infographic shares the potential benefits of putting “Smart Insurance” at the center of the enterprise:
IoT implementation within organizations varies from basic one-offs to advanced ecosystems. IoT maturity has three phases:
- Primitive: Basic partnerships with equipment vendors through whom data is purchased via web service calls. Alternatively, the carrier can leverage customer-provided data to generate predictive insights, but the investment is limited to cloud-based analytical services.
- Intermediate: IoT-linked insurance (and related) services delivered via partnerships with platform owners and machine/equipment vendors. Data is pushed to the insurer’s IoT cloud by the gateway provider.
- Mature: Carrier ownership and operation of an end-to-end IoT ecosystem. The insurer deploys sensors to collect and aggregate customer data on their IoT platform generating valuable insights — and unique value proposition for customers. The data can be monetized by sharing it with third parties offering complementary analytical solutions based on the data.
Depending on the carrier role in IoT implementation, different types of “Data Sharing” models can be formed, including:
- Basic: An entry-level partnership with a vendor. Provides basic data on proof of ownership, proof of activation, power on data, etc.
- Developing: An intermediate partnership with equipment/machine/gateway vendors, with data available on asset usage, breakdowns, and maintenance. Products incorporate usage-based pricing and underwriting.
- Advanced: Real-time data is generated by equipment/machine/gateways developed in-house, or by a qualified service provider. The carrier’s ownership of the data helps derive meaningful insights and the full benefits from data-driven product offerings, like real-time event handling and straight-through claims processing.
Another way of doing this is to create a partner ecosystem by collaborating with third-party data providers. In this case, real-time data is owned by the carrier or connected-device vendors and equipment manufacturers, typically transmitted by communication infrastructure providers to the insurer’s cloud platform.
Key challenges and considerations with respect to IoT implementation include:
- Product differentiation using customer profiles
- Data ownership and quality
- Data security and privacy
- A highly dynamic and competitive partnership environment
- Device and architectural capabilities
- Transformation, adoption and alignment with internal data and processes
- Cost efficiencies with a focus on IoT system optimization, regular low-cost assessments and a long-term ecosystem supportive of future technology changes, including upgrades
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Digital Insurer's CommentsWe write a lot about game-changing technologies in insurance and IoT is the mother of all game-changers. With commercial IoT spending growing at a 17% annual rate, projected to hit $1.3 trillion by 2019, ‘smart’ assets are supplanting ‘dumb’ ones at an accelerating rate. Why is this such a big deal?
Because ‘smart’ assets digitize risk itself. A smart car, for example, tells an insurer everything it needs to know about a specific driver in no uncertain terms. And two smart cars in an accident tell two insurers everything they need to know about fault and payout. Trust, long the main selling point of differentiation amongst insurers, will eventually be supplanted by digital fact in the insurance equation.
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