Data has undoubtedly altered the way the insurance industry works, enabling companies to access more information about customers and allowing them to offer the potential for cheaper premiums and rewards.
Telematics, wearables, and connected homes are the main established examples.
The health sector is where big datas impact is most evident, with wearable technology increasingly used by large insurers, which offer cheaper premiums, reward schemes and health benefits in exchange for personal data. Statista has forecast that the wearable device market value will value US$12.7 billion, and 13.45 million devices will be shipped worldwide, in 2018.
Similar to all aspects of insurance technology (insurtech), it is the large insurers which are struggling most with modernizing systems and approaches. This is due to ingrained processes and an inability to tolerate failure, meaning that innovation is often stymied. Other challenges, such as difficulties in attracting innovative talent and entrenched IT systems, also prevent rapid change. Smaller companies and startups have often developed through trial and error, and therefore have more tolerance for failure. They also tend to be managed by more technically aware people and are less reliant on dated IT systems that would be cost-prohibitive to update.
As insurers increasingly hold greater amounts of data, often of an extremely personal nature, the potential damage of a cyberattack increases. The data is already expensive to store safely, but costly fines, set to be introduced in the new EU data protection regulation of 2018, will force insurers to invest in cybersecurity.
Many insurers are looking to reduce paid claims by preventing them. This is the case in healthcare, and is also seen in the property and motor categories with connected homes and telematics. As well as reducing claims, this will also help insurers to build relationships with customers, as traditionally contact only occurs when a policy is due for renewal.
The benefits of big data are still being deliberated. It is argued that the more information that becomes available to insurers the more they are able to price niche policies, or at least have peer-to-peer insurers cover them; diabetes is the most commonly cited example. Cheaper premiums for customers who are able to improve their lifestyle are another potential advantage.
The argument against the use of big data is that more information will lead to the creation of an insurance underclass, where sections of society become uninsurable because insurers will work out that it is simply not profitable to offer policies to certain individuals. This is something on which the head of the Financial Conduct Authority (FCA), Andrew Bailey, has expressed concerns, and he has suggested that there should be limits on the use of big data to ensure groups of customers are not unfairly penalized. An FCA inquiry into the use of big data concluded that while no immediate action would be taken, the situation would be closely monitored.
Many aspects of taxi, hotel, travel and entertainment services have been digitalized to offer customers quick and easy access. Spotify, Netflix, Amazon, eBay Uber and Airbnb have all revolutionized their respective sectors. All these services hold some level of personal information and tailor offers around them; the lack of ability to tailor products is a primary reason for the insurance sectors inability to compete. An individualized approach, based on customer data and pushing other products based on life events, is the next step the industry will take to offer customers the level of service they have come to expect from nearly every other sector.
The challenge for insurers is to both figure out how to make money from the vast amounts of data to which they have access, and to offer customers the levels of digital service they have come to expect from nearly all other sectors.
Many aspects of taxi, hotel, travel and entertainment services have been digitalized to offer customers quick and easy access. Spotify, Netflix, Amazon, eBay Uber and Airbnb have all revolutionized their respective industries.
The report looks at how far the insurance sector has come, and what still needs to be done to achieve this.
Reasons To Buy
The report's primary research includes interviews with experts on big data's impact on the insurance industry.
The report breaks the market down by different insurance lines, including life, health and motor. It discusses trends, and gives examples and insights from relevant primary research.
It offers coverage and analysis of key examples of big data use in large-scale companies and startups.
It provides insights into upcoming regulation, and how it could impact the market.
The report uses of exclusive data from Timetrics survey on the future of technology in insurance.
Big data is a growing factor in the insurance industry.
It helps insurers to develop more accurate premiums, and rewards customers for safer behavioral trends.
The health and motor lines lead the way, with property not far behind. More complicated pension policies in the life segment are harder to tailor based on data.
Incumbent insurers are beginning to adapt and use data to their advantage, but the transition is easier for smaller, nimbler companies that are less reliant on systems and processes.
Table of Contents
1 EXECUTIVE SUMMARY
2 INDUSTRY OVERVIEW
2.1 All Sectors
2.2 Life and Health Insurance
2.3 Motor Insurance
2.4 Agricultural Insurance
3 MONETIZATION AND PRODUCTS
9 ABOUT TIMETRIC
9.1 Contact Timetric
9.2 About Timetric
9.3 Timetrics Services
List of Figures
Figure 1: Total Value of the Global Wearable Device Market, (US$ Billion) 2015
Figure 2: Total Value of the Global Wearable Device Market, (%) 2015
Figure 3: Global Appeal of Smart Homes (%)
Figure 4: Main Reasons for Running a Smart Home, (%), 2015
Figure 5: Barriers to Running a Smart Home, (%), 2015
Figure 6: Please Rate the Damage Typically Caused by the Following Outcomes of a Cyber attack?
Figure 7: How Can Cyber-Risk Best be Minimized?
Figure 8: How Much Will the Following Technologies Affect Insurance During 20162021? (%)