Global Big Data In Insurance Market Research Report 2017

  • KMI-172489
  • Feb 2018
  • 114 pages
  • Services
The Global Big Data in Insurance Market Research Report Forecast 2017-2021 is a valuable source of insightful data for business strategists. It provides the Big Data in Insurance industry overview with growth analysis and historical & futuristic cost, revenue, demand and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Big Data in Insurance market study provides comprehensive data which enhances the understanding, scope and application of this report.
This report provides comprehensive analysis of
Key market segments and sub-segments
Evolving market trends and dynamics
Changing supply and demand scenarios
Quantifying market opportunities through market sizing and market forecasting
Tracking current trends/opportunities/challenges
Competitive insights
Opportunity mapping in terms of technological breakthroughs

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 data’s 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.

Summary

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 sector’s 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.

Scope

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.

Global Big Data in Insurance Market: Regional Segment Analysis
North America
Europe
China
Japan
Southeast Asia
India

Reasons for Buying this Report
This report provides pin-point analysis for changing competitive dynamics
It provides a forward looking perspective on different factors driving or restraining market growth
It provides a six-year forecast assessed on the basis of how the market is predicted to grow
It helps in understanding the key product segments and their future
It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments
Chapter 1 Big Data in Insurance Market Overview
1.1 Product Overview and Scope of Big Data in Insurance
1.2 Big Data in Insurance Market Segmentation by Type
1.2.1 Global Production Market Share of Big Data in Insurance by Type in 2015
1.2.1 Type 1
1.2.2 Type 2
1.2.3 Type 3
1.3 Big Data in Insurance Market Segmentation by Application
1.3.1 Big Data in Insurance Consumption Market Share by Application in 2015
1.3.2 Application 1
1.3.3 Application 2
1.3.4 Application 3
1.4 Big Data in Insurance Market Segmentation by Regions
1.4.1 North America
1.4.2 China
1.4.3 Europe
1.4.4 Southeast Asia
1.4.5 Japan
1.4.6 India
1.5 Global Market Size (Value) of Big Data in Insurance (2012-2021)

Chapter 2 Global Economic Impact on Big Data in Insurance Industry
2.1 Global Macroeconomic Environment Analysis
2.1.1 Global Macroeconomic Analysis
2.1.2 Global Macroeconomic Environment Development Trend
2.2 Global Macroeconomic Environment Analysis by Regions

Chapter 3 Global Big Data in Insurance Market Competition by Manufacturers
3.1 Global Big Data in Insurance Production and Share by Manufacturers (2015 and 2016)
3.2 Global Big Data in Insurance Revenue and Share by Manufacturers (2015 and 2016)
3.3 Global Big Data in Insurance Average Price by Manufacturers (2015 and 2016)
3.4 Manufacturers Big Data in Insurance Manufacturing Base Distribution, Production Area and Product Type
3.5 Big Data in Insurance Market Competitive Situation and Trends
3.5.1 Big Data in Insurance Market Concentration Rate
3.5.2 Big Data in Insurance Market Share of Top 3 and Top 5 Manufacturers
3.5.3 Mergers & Acquisitions, Expansion

Chapter 4 Global Big Data in Insurance Production, Revenue (Value) by Region (2012-2017)
4.1 Global Big Data in Insurance Production by Region (2012-2017)
4.2 Global Big Data in Insurance Production Market Share by Region (2012-2017)
4.3 Global Big Data in Insurance Revenue (Value) and Market Share by Region (2012-2017)
4.4 Global Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.5 North America Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.6 Europe Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.7 China Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.8 Japan Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.9 Southeast Asia Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)
4.10 India Big Data in Insurance Production, Revenue, Price and Gross Margin (2012-2017)

Chapter 5 Global Big Data in Insurance Supply (Production), Consumption, Export, Import by Regions (2012-2017)
5.1 Global Big Data in Insurance Consumption by Regions (2012-2017)
5.2 North America Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)
5.3 Europe Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)
5.4 China Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)
5.5 Japan Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)
5.6 Southeast Asia Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)
5.7 India Big Data in Insurance Production, Consumption, Export, Import by Regions (2012-2017)

Chapter 6 Global Big Data in Insurance Production, Revenue (Value), Price Trend by Type
6.1 Global Big Data in Insurance Production and Market Share by Type (2012-2017)
6.2 Global Big Data in Insurance Revenue and Market Share by Type (2012-2017)
6.3 Global Big Data in Insurance Price by Type (2012-2017)
6.4 Global Big Data in Insurance Production Growth by Type (2012-2017)

Chapter 7 Global Big Data in Insurance Market Analysis by Application
7.1 Global Big Data in Insurance Consumption and Market Share by Application (2012-2017)
7.2 Global Big Data in Insurance Consumption Growth Rate by Application (2012-2017)
7.3 Market Drivers and Opportunities
7.3.1 Potential Applications
7.3.2 Emerging Markets/Countries

Chapter 8 Big Data in Insurance Manufacturing Cost Analysis
8.1 Big Data in Insurance Key Raw Materials Analysis
8.1.1 Key Raw Materials
8.1.2 Price Trend of Key Raw Materials
8.1.3 Key Suppliers of Raw Materials
8.1.4 Market Concentration Rate of Raw Materials
8.2 Proportion of Manufacturing Cost Structure
8.2.1 Raw Materials
8.2.2 Labor Cost
8.2.3 Manufacturing Expenses
8.3 Manufacturing Process Analysis of Big Data in Insurance

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
9.1 Big Data in Insurance Industrial Chain Analysis
9.2 Upstream Raw Materials Sourcing
9.3 Raw Materials Sources of Big Data in Insurance Major Manufacturers in 2015
9.4 Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders
10.1 Marketing Channel
10.1.1 Direct Marketing
10.1.2 Indirect Marketing
10.1.3 Marketing Channel Development Trend
10.2 Market Positioning
10.2.1 Pricing Strategy
10.2.2 Brand Strategy
10.2.3 Target Client
10.3 Distributors/Traders List

Chapter 11 Market Effect Factors Analysis
11.1 Technology Progress/Risk
11.1.1 Substitutes Threat
11.1.2 Technology Progress in Related Industry
11.2 Consumer Needs/Customer Preference Change
11.3 Economic/Political Environmental Change

Chapter 12 Global Big Data in Insurance Market Forecast (2017-2021)
12.1 Global Big Data in Insurance Production, Revenue Forecast (2017-2021)
12.2 Global Big Data in Insurance Production, Consumption Forecast by Regions (2017-2021)
12.3 Global Big Data in Insurance Production Forecast by Type (2017-2021)
12.4 Global Big Data in Insurance Consumption Forecast by Application (2017-2021)
12.5 Big Data in Insurance Price Forecast (2017-2021)

Chapter 13 Appendix

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