There is a Large Untapped Potential to Collect Relevant Data about Policy-holders and their Homes

A significant and ever-increasing amount of data is being generated by people and their homes. Whether you look at the TV, the washing machine, the fridge, your Google home or Alexa devices, data are everywhere and can be collected to better service home owners. These data have the potential to provide much more accurate details on a policy-holder’s behaviours, but also their wants and needs as well as their risk profile.

This chapter seeks to describe the benefits for both the customer and the insurer of collecting and analysing a wider range of data sets to deliver personalized insights and services. The chapter sets out a few guiding principles and illustrates those principles with examples focused on home insurance. The same scenarios are equally applicable to other types of insurance offerings.

Helping Policy-holders Understand Actions or Characteristics that Put them at an Increased Risk of a Claim

No rational policy-holder desires the interruption caused by a claim. They may not understand how their behaviours or circumstances affect or increase their likelihood of suffering such an event. However, combined together, policy-holders’ specific data and more general environmental data can help an insured manage his or her risk better.

Policy-holders’ specific data are captured during the quote process, where very specific questions are asked and then enriched with specific data services. Other mechanisms already exist to allow the policy-holders to share a wide set of real time data (e.g. personal and social data) with their insurer and others involved through this process.

It is fair to say that an applicant may not want to share his data because of the risk of being declined. Nonetheless, if he did, both the applicant and his insurer would derive clear benefits from the insights this provides.

It is also clear that the policy-holder and the insurer can benefit from ranges of data captured by a variety of data sources via Internet of Things (IoT) technology. Examples include home occupancy, alerts, and event indicators such as the triggering of a smoke alarm or the usage of a variety of smart home sensors, including energy management systems. And, while a policy-holder can use home automation to achieve the protection he or she seeks, an insurer using the same data sets to monitor large numbers of policy-holders can benefit from the iterative learning of optimizing pricing and claims support activities. Blending these resources can lead to improved series of outcomes when an actual or potential claim event occurs.

1. If a fire alarm is triggered in a smart home, and the devices have not registered a human presence in the home at the time of the incident, the home devices can still trigger internal fire alarms and could take greater steps to alert the home owners, the local fire brigade, and the insurer of the incident. While this may result in the production of false positives, the feedback loop would enable the increased accuracy over time of the algorithms used to perform those services.
2. Smart devices able to suspect, detect, and measure unusual uses of water (e.g. leaks or open taps) based on volumes, velocity, location, and other patterns are known as leak bots. Devices functioning on their own can provide an alert to the policy-holder. Combining these bots with occupancy indicators could increase the accuracy assessment of potential home perils, ensure that actions are taken before they become disastrous, and increase the validity of the preventative actions taken – for example, halting a flow of water using an electronic stopcock rather than just notifying the insured.

Environmental data are not directly applicable to the policy-holder but are useful indicators. The amounts of data currently available can be used to enhance insurers’ relationship with their policy-holders in many ways.

1. Historical measurements of climate factors could enable an insurer to link burglary events to changes in weather patterns – meaning temperature, humidity, and other key factors. An insurer that successfully interrogated its claims data to understand how the environment relates to incurred claims could implement a program of tailored messaging to reduce the risk of theft. A historical increase in property crime between June to August was identified by one study.¹ It is clearly possible that there may have been a strong correlation between opened windows and increased risk of night burglaries as policy-holders forget to close their windows. It could also be possible that the propensity for criminal acts increases during these summer months. Insurers could take the insight and recommend actions to ensure that their policy-holders are protected from the risk of a claim.
2. Monitoring real-time crime and fire event data within both local and wider communities can lead to early preventative actions. The limited insight provided by current open source crime reports allows month-by-month analyses of crime patterns in specific areas. An average policy-holder is unlikely to have the ability and time to monitor and analyse crime patterns with his or her own community; however, insurers focused on enhancing prevention could provide alerts for policy-holders and wider communities, and not limit this to property crimes. The same analysis could lead to targeted forms of communication between policy-holders and their insurers in particular for areas prone to a variety of daily incidents and crimes. Such activities would shift the insurer–policy-holder relationship to a trusted advisory one from just post-event assistance.

Validating that Policy-holders Understand their Obligations

A number of exclusions and conditions exist within insurance policies to manage insurers’ risk exposure and prevent negligent claims. Insurers can use data collected from IoT devices to identify potential scenarios where policy-holders might be prone to be acting within exclusion zones. By having the data to initiate a conversation, the insurer can ensure that the policy-holder is aware of the exclusions and understands the rationale behind them. This can reduce the likelihood of denied claims due to misunderstood terms, enhancing the overall customer experience. Insurers can use this mechanism to verify that the policy-holders have represented themselves, their risk, and exposure correctly.

Examples of how this can be of benefit include:

1. Making a policy-holder aware that their property seemed unoccupied for a long period of time and reminding them that their home can only be unoccupied for no more than an agreed number of days.
2. Making a policy-holder aware during winter months that their home temperature is too low and could result in burst pipes.
3. Leveraging electronic access to digitize asset inventories and assist in determining the true insured content value of a home.

Making policy-holders and people in general aware of home insurance risks can help them understand and manage their exposure better. However, the abundance of non-relevant information potentially extracted from IoT sources can lead to information overload and apathy. One way to overcome apathy is to personalize the information on risks through awareness and education campaigns. Using claims data from policy-holders with similar characteristics can help shape guidance parameters and result in greater attention paid to share recommendations.

Resolving the Issues Related to the Collection and Aggregation of Significant Pools of Untapped Data Sources

Organizations that want to lead the way in the access and usage of IoT-based data are facing a number of challenges arising from (1) the collection of new data sources, (2) the aggregation of these sources to deliver relevant new insight, and (3) the analysis of such insight to deliver new products and services that augment users’ lives.

The issues we believe large enterprises will encounter are several as highlighted below:

• As no standard is yet available for IoT data, insurers will need to transform that data into forms that can be utilized for analysis.
• To easily incorporate external data sets and meet internal data security standards, systems will need to be upgraded.
• To satisfy policy-holders’ privacy expectations, especially with respect to listening devices, such as Google Home and the Amazon Echo, insurers will need to develop and share policies on connectivity and data usage.
• To avoid reputational risk linked with data usage and insurability, insurers will need to focus on prevention rather than identifying foundational issues that will make the policy-holder worse off than before. This issue is key.

The significant benefit that may result from using a variety of data sources for the purpose of providing preventative insights and guidance should outweigh the risk of making specific risk types uninsurable. However, a mechanism by which risks made uninsurable by new insights outside of the policy-holder’s control or due to poor insurer interpretation may remain. This is where industry bodies, the regulators, and the law will increasingly be involved in such initiatives.

Conclusion

Data changes everything because policy-holders can now better understand their exposure and manage their risk themselves. For the insurer this means that increased transparency will drive a focus on more prevention-focused products and services. Such a scenario changes the nature of insurance from just financially indemnifying policy-holders to continuously managing and mitigating policy-holder risks for the wider good. These provide clear benefits to both stakeholder groups, because it educates the insured on the right behaviour to achieve peace of mind and reduces overall claims costs for the insurer. This also reduces the current focus on commoditizing insurance to providing more personalized servicing ranging from:

• tailored guidance to the policy-holders in respect of their risk profile such that policy-holders do not have to interpret advice and understand why it is applicable to them;
• confirmation that policy-holders’ actions and characteristics are in line with the statements made explicitly or implicitly when taking cover;
• more effective triage of fraudulent claims, reducing the pain of the claims experience for policy-holders.

The key challenges to this brave new world are around consumer acceptance of sharing the data, the regulatory concerns that will arise from managing an abundance of personal and insightful data, and the protection of policy-holders that are deemed to be uninsurable by the insights available from the data collected.