Connected Home – Driving Innovation for P&C Insurance Carriers!

The connected car, home and lifestyle are driving innovation for the P&C and life Insurers. Sensors and Devices along with M2M communication is creating new business models for insurers. By 2016, the global connected home market is expected to reach $235B. P&C Insurance carriers are trying to find the right entry point in this complex ecosystem. As smart home technology evolves, insurers may have the opportunity to use real-time telemetric data for the assessment of risk and prevention of loss. Home automation or “smart home” technology is making inroads through popular consumer solutions and already provides opportunities for underwriters to recognize lower risk factors among customers. The smart home represents a significant market opportunity

  • Customer Retention and Cross Sell /Up Sell
  • Better risk management and loss prevention
  • Value Added services
    • Catastrophe Response and Alert
    • Preventive Care and Maintenance
    • Repair and Restoration Services
    • Home monitoring
  • Underwriting Optimization
  • Claims Optimization and Fraud Prevention

Most carriers are still in the early stages of IOT – smart Home market. Within the connected home, insurers are focused primarily on these different data sources

  • Home Security and Monitoring – Alarms, Smart Carpet, Video, Smart Door, etc
  • Utility Companies – Electricity, Gas, Water, Cable companies
  • External Weather – Flood, Hurricane, Tornadoes, Hails, Snow, Earthquake, Lightening, etc
  • Connected Appliances – Refrigerator, Oven, Microwave, Dishwasher, Washer-Dryers, etc
  • Solar Panels
  • Thermostats
  • Smoke and Fire
  • Smoke / Carbon Monoxide Detectors
  • Climate Control Systems
  • Pet Monitoring Systems

Both Google and Apple have moved aggressively into this space. The
market is in an early adoption phase and it is unclear which brands will win out. The market is breaking out from niche solutions for the wealthy, e.g. monitored security systems, to the mass market. Google’s Nest has established partnerships with American Family Insurance and Liberty Mutual Insurance to offset the costs of a Nest Protect smoke detector, and establish a monthly discount for homes that link their Nest smoke detectors to the insurance firms. The application and integration of these new data sources with underwriting and claims application will be bring tremendous value to insurance industry. The carriers will be looking for data aggregation platform that will provide the data mashup and services that can be consumed as API in their current application portfolio.

Consumers will likely see the connected home as a set of possibilities—expanded entertainment options, greater physical comfort, a connected lifestyle, and reduced risk—and use it to seek broad solutions to everyday tasks and big life events. As they embrace propositions that seamlessly combine these possibilities, consumers will blur existing market boundaries. Players such as AT&T are even linking connected car and smart home technologies so users can control their homes from a vehicle dashboard. Very soon like auto industries, very soon, home builders will offer smart home and integrated capabilities.

Compared to the connected car, the connected home is a less mature, less certain, and more complex insurance market. Carriers and telcos already have expanded from competing with security monitoring companies such as ADT into smart home offerings such as Comcast Xfinity Home, Time Warner Cable’s Intelligent Home, or Home by SFR in France. And AT&T’s Digital Life offering is building on open standards and protocols to integrate with products from many companies, not just their own.

A USA State of the Smart Home Report in May 2014 based on data from more than 900 U.S. adults, found that:

  • 86% ranked property loss protection as a top reason for a smart home system
  • 78% of consumers ranked energy management as one of the top features that matter most in the smart home
  • 67% ranked personal and family security as the number one reason for using a smart home system
  • 52% of pet owners listed pet monitoring as one of the top five most important reasons for using a smart home service

A 2013 report from Berg Insight estimates that North America is the most advanced region in the world for smart home solutions with an installed base of 3.5 million systems at the end of 2012. Between 2012 and 2017 the installed base is forecasted to grow at a compound annual growth rate of 55.0 percent to reach 31.4 million smart home systems.

The next logical step for insurers will be to move beyond knowing that devices are in place, to monitoring the data real-time from such devices. This would be similar to telematics from cars. Limited amount of private information will be expected in order to offer a premium discount. Premium discounts will not just depend on knowing a device is there, but on actual intelligence from the device. Insurers will also monitor use of the house to see if certain behaviors lead to increased risk. The insurance will be usage based like utility model. The monthly premium charged will in some cases become a variable figure, based on behavior that month and perceived risk incurred. This will incentivise the homeowner to modify home use, as has happened with car telematics.

In order to offer these products and services, either device data will need to become ‘open’ so that anyinsurer can use it, or insurers will need to forge strategic partnerships with major players in the connected home arena. If the former, customer loyalty and switching will be an issue. If the latter, picking the right partner will become key.


Big Data Analytics in Life Insurance and Disability Insurance

Big Data is rapidly evolving and affecting the life insurance landscape. Big Data Analytics has led to significant innovation and new product development in life science and disability insurance market place. The success of Big Data innovation lies in having the courage and judgement to trust the insight gained, and the strategic will to implement it.

Data providers have now become very critical to many successful use cases. Life insurers can now ask for external data sources to create a complete risk profile and understand their customer before underwriting life or disability insurance. The analytical risk models and scoring can be expanded the footprint to include

  1. Life Style data – This includes data about your house, house members, and neighborhood, profession, employer, recreation activities including sports, cars driven, previous claims, etc.
  2. Healthcare data – This data is available with healthcare providers and laboratories. This data is very crucial to determine the mortality and morbidity risks.
  3. Retail/Grocery/SuperMarket data – This can be very new data set that can be injected into your risk scoring. Retailers store massive amount of data that can identify your family and individual health habbits and life style. The challenge is getting access to these data sets
  4. Banking/Credit card/Retirement savings data – This data set can provide insight into individual’s financial stress, saving pattern and individual’s risk score.

As per BCG Survey in June 2014, Insurers are using big data and advanced analytics to improve business processes and expand into new markets, thereby generating significant revenues and profits. Others are building long-term, trusted relationships with customers. These insurers are offering valuable new or improved products and services in exchange for personal data that customers provide voluntarily. The new offerings may help clients improve their health or may provide access to insurance for people who are considered risky or expensive to serve. The landscape is changing. Companies that get ahead of these shifts will discover new opportunities for efficiency, growth, and innovation.

Companies are also using data to understand distribution. Understanding the quality of business written by different company agents, tied channels or independent brokers is useful. Models are emerging that identify and score the better agents, and this information is then used by the insurer to manage agents and provide incentives for agents to improve behavior. Agents can be scored on various factors, including:

■ Early lapse experience and/or policies not taken up

■ Comparisons of disclosure rates identifying agents or brokers that are good at encouraging policyholder disclosure

■ Sales figures, such as volumes, policy size, etc.

These models also make it possible for the insurer to manage distribution and identify individual agents, brokers, or even broker agency or networks that need improvement.

Opportunities to use internal data are also available to insurers operating in the group environment. Information may be available from sources within the insurer on the employer and/or the employees of a particular group. This could allow the insurer to more accurately understand the risks. An example may be where the insurer has absence management program data, which could be linked to project disability and mortality rates. Such sources of data tend to be more useful where groups are too small to be rated fully on their own experience. It may, however, still be useful for conditions that are changing from year to year for a larger group

There are risks associated with Big Data, although this article did not cover them. Data privacy continues to be an important issue, and recent revelations of government surveillance have further heightened awareness of it.

Many insurance companies are tapping into large, fast-moving, complex streams of big data and applying advanced analytical techniques to transform the way they do business. All life insurers are now converts to the idea of big data in principle. But using big data seems to be low on the executive agenda. In practice, the life insurance industry as a whole has been slow in making use of the data they have access to. The truly transformative potential of these strategies will take time to play out. Insurers that excel at these and other strategies to intensify the customer relationship and move into new markets will catch up to—or even surpass—more nimble companies that are already in the game. Over the long term, these approaches will open up important avenues for growth and innovation for companies willing to experiment now. They will win the best clients, reduce risk, increase loyalty, and create more opportunities to cross-sell products and services.The rest will risk falling behind or ceding the most attractive customer relationships and emerging markets to others.

Dynamic Insurance Pricing – Telematics Analytics & Behavioural monitoring

In the auto insurance industry, the terms telematics and usage-based insurance (UBI) are often used interchangeably – but they are actually two different concepts. Usage-based insurance is a broader concept that can be broken down into two categories: self-reporting policies and telematics-based policies. There are two main types of telematics-based insurance products: Pay-as-you-drive (PAYD) and Pay-how-you-drive (PHYD). Vehicle telematics refers to automobile systems that combine Global Positioning System tracking and other wireless communications for automatic roadside assistance and remote diagnostics. Telematics refers to solutions that are based on information flowing to and/or from a vehicle. When implemented, telematics have the potential to increase operational efficiency and improve driver safety in a number of ways; for example:

  • GPS technology tracks a vehicle’s location, mileage, and speed. Fleet and Distribution companies can use this information to optimize routes and scheduling efficiency.
  • Communications technology promotes connectivity between drivers and dispatch.
  • Sensors monitor vehicle diagnostics which can then be used to streamline vehicle maintenance.
  • Accelerometers measure changes in speed and direction, cameras that monitor road condition, and drivers’ actions. This information can be used to improve driver performance through a one-on-one or in-vehicle coaching program

Insurers want access to a database of telematics data to help them set personalized premiums for individual drivers, but arrangements governing how that information is gathered, managed and accessed could be subject to scrutiny by competition regulators. Telematics data can constitute personal data, and therefore fall subject to data protection laws, on the basis that it records the activities of individual drivers, or a number of individuals. Insurers will need to be able to make sense of this data via a model with predictive capabilities based on frequency of driving, hard braking, sharp turns, time of the day, and a handful of other factors, to determine the personalized premium for each customer based on risk profile. UBI programs offer many advantages to insurers, consumers and society. Linking insurance premiums more closely to actual individual vehicle or fleet performance allows insurers to more accurately price premiums. This increases affordability for lower-risk drivers, many of whom are also lower-income drivers. It also gives consumers the ability to control their premium costs by incenting them to reduce miles driven and adopt safer driving habits. Fewer miles and safer driving also aid in reducing accidents, congestion, and vehicle emissions, which benefits society.

Auto insurance is going through transformational changes due to weather, telematics, social data and advancement in auto technology. The next generation of insurance pricing will be very similar to utility industry pricing model that will be driven by actual usage, rate tier based on timing of the day, location and the customer risk profile. As auto industry gets matured with driverless car technology, the insurance will be driven by risk profile of car and technology vs. risk profile of individual customer. All data from self driven/driverless car will be fed back to isnruance companies through telematics data. Three insurance suppliers and an auto parts maker have warned in their most recent annual reports that driverless cars and the technology behind them could one day disrupt the way they do business. The industry collected $107.4 billion in passenger car auto insurance premiums in 2013, the latest year for which figures were available, according to the Insurance Information Institute. Self-driving cars could have other effects as well. Insurers expect car- and software-makers to face litigation when crashes do happen, shifting at least some of the expense from consumer auto insurance to commercial liability policies. A 2013 analysis by PricewaterhouseCoopers suggested that the company thinks driverless cars won’t impact their bottom line anytime soon, but that it will, eventually. The use of telematics helps insurers more accurately estimate accident damages and reduce fraud by enabling them to analyze the driving data (such as hard breaking, speed, and time) during an accident. This additional data can also be used by insurers to refine or differentiate UBI products. Additionally, the ancillary safety benefits offered in conjunction with many telematics-based UBI programs also help to lower accident and vehicle theft related costs by improving accident response time, allowing for stolen vehicles to be tracked and recovered, and monitoring driver safety. Telematics also allow fleets to determine the most efficient routes, saving them costs related to personnel, gas, and maintenance.

According to SMA Research, approximately 36 percent of all auto insurance carriers are expected to use telematics UBI by 2020. Based on a May 2014  CIPR survey of 47 U.S. state and territory insurance departments, in all but five jurisdictions – California, New Mexico, Puerto Rico, Virgin Islands, and Guam – insurers currently offer telematics UBI policies. In twenty-three states, there are more than five insurance companies active in the telematics UBI market

As the move toward dynamic pricing becomes more prominent, some firms will likely go farther than others. A lot of analysis and research will need to be done to determine how much of a risk can effectively be determined by static versus dynamic factors.

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