Big Data originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.
Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The insurance industry is no exception to this trend, where Big Data has found a host of applications ranging from targeted marketing and personalized products to usage-based insurance, efficient claims processing, proactive fraud detection and beyond.
This research estimates that Big Data investments in the insurance industry will account for more than $2.4 Billion in 2018 alone. Led by a plethora of business opportunities for insurers, reinsurers, insurance brokers, InsurTech specialists and other stakeholders, these investments are further expected to grow at a CAGR of approximately 14% over the next three years.
The report presents an in-depth assessment of Big Data in the insurance industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 8 application areas, 9 use cases, 6 regions and 35 countries.
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
Scope
Big Data ecosystem
Market drivers and barriers
Enabling technologies, standardization and regulatory initiatives
Big Data analytics and implementation models
Business case, application areas and use cases in the insurance industry
20 case studies of Big Data investments by insurers, reinsurers, InsurTech specialists and other stakeholders in the insurance industry
Future roadmap and value chain
Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
Strategic recommendations for Big Data vendors and insurance industry stakeholders
Market analysis and forecasts from 2018 till 2030
Market forecasts are provided for each of the following submarkets and their subcategories:
Hardware, Software & Professional Services
Hardware
Software
Professional Services
Horizontal Submarkets
Storage & Compute Infrastructure
Networking Infrastructure
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services
Application Areas
Auto Insurance
Property & Casualty Insurance
Life Insurance
Health Insurance
Multi-Line Insurance
Other Forms of Insurance
Reinsurance
Insurance Broking
Use Cases
Personalized & Targeted Marketing
Customer Service & Experience
Product Innovation & Development
Risk Awareness & Control
Policy Administration, Pricing & Underwriting
Claims Processing & Management
Fraud Detection & Prevention
Usage & Analytics-Based Insurance
Other Use Cases
Regional Markets
Asia Pacific
Eastern Europe
Latin & Central America
Middle East & Africa
North America
Western Europe
Country Markets
Argentina
Australia
Brazil
Canada
China
Czech Republic
Denmark
Finland
France
Germany
India
Indonesia
Israel
Italy
Japan
Malaysia
Mexico
Netherlands
Norway
Pakistan
Philippines
Poland
Qatar
Russia
Saudi Arabia
Singapore
South Africa
South Korea
Spain
Sweden
Taiwan
Thailand
UAE
UK
USA
Key Findings
In 2018, Big Data vendors will pocket more than $2.4 Billion from hardware, software and professional services revenues in the insurance industry. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for nearly $3.6 Billion by the end of 2021.
Through the use of Big Data technologies, insurers and other stakeholders are beginning to exploit their data assets in a number of innovative ways ranging from targeted marketing and personalized products to usage-based insurance, efficient claims processing, proactive fraud detection and beyond.
The growing adoption of Big Data technologies has brought about an array of benefits for insurers and other stakeholders. Based on feedback from insurers worldwide, these include but are not limited to an increase in access to insurance services by more than 30%, a reduction in policy administration workload by up to 50%, prediction of large loss claims with an accuracy of nearly 80%, cost savings in claims processing and management by 40-70%, accelerated processing of non-emergency insurance claims by a staggering 90%; and improvements in fraud detection rates by as much as 60%.
In addition, Big Data technologies are playing a pivotal role in facilitating the adoption of on-demand insurance models - particularly in auto, life and health insurance, as well as the insurance of new and underinsured risks such as cyber crime.
Chapter 2: An Overview of Big Data 2.1 What is Big Data? 2.2 Key Approaches to Big Data Processing 2.2.1 Hadoop 2.2.2 NoSQL 2.2.3 MPAD (Massively Parallel Analytic Databases) 2.2.4 In-Memory Processing 2.2.5 Stream Processing Technologies 2.2.6 Spark 2.2.7 Other Databases & Analytic Technologies 2.3 Key Characteristics of Big Data 2.3.1 Volume 2.3.2 Velocity 2.3.3 Variety 2.3.4 Value 2.4 Market Growth Drivers 2.4.1 Awareness of Benefits 2.4.2 Maturation of Big Data Platforms 2.4.3 Continued Investments by Web Giants, Governments & Enterprises 2.4.4 Growth of Data Volume, Velocity & Variety 2.4.5 Vendor Commitments & Partnerships 2.4.6 Technology Trends Lowering Entry Barriers 2.5 Market Barriers 2.5.1 Lack of Analytic Specialists 2.5.2 Uncertain Big Data Strategies 2.5.3 Organizational Resistance to Big Data Adoption 2.5.4 Technical Challenges: Scalability & Maintenance 2.5.5 Security & Privacy Concerns
Chapter 3: Big Data Analytics 3.1 What are Big Data Analytics? 3.2 The Importance of Analytics 3.3 Reactive vs. Proactive Analytics 3.4 Customer vs. Operational Analytics 3.5 Technology & Implementation Approaches 3.5.1 Grid Computing 3.5.2 In-Database Processing 3.5.3 In-Memory Analytics 3.5.4 Machine Learning & Data Mining 3.5.5 Predictive Analytics 3.5.6 NLP (Natural Language Processing) 3.5.7 Text Analytics 3.5.8 Visual Analytics 3.5.9 Graph Analytics 3.5.10 Social Media, IT & Telco Network Analytics
Chapter 4: Business Case & Applications in the Insurance Industry 4.1 Overview & Investment Potential 4.2 Industry Specific Market Growth Drivers 4.3 Industry Specific Market Barriers 4.4 Key Application Areas 4.4.1 Auto Insurance 4.4.2 Property & Casualty Insurance 4.4.3 Life Insurance 4.4.4 Health Insurance 4.4.5 Multi-Line Insurance 4.4.6 Other Forms of Insurance 4.4.7 Reinsurance 4.4.8 Insurance Broking 4.5 Use Cases 4.5.1 Personalized & Targeted Marketing 4.5.2 Customer Service & Experience 4.5.3 Product Innovation & Development 4.5.4 Risk Awareness & Control 4.5.5 Policy Administration, Pricing & Underwriting 4.5.6 Claims Processing & Management 4.5.7 Fraud Detection & Prevention 4.5.8 Usage & Analytics-Based Insurance 4.5.9 Other Use Cases
Chapter 5: Insurance Industry Case Studies 5.1 Insurers 5.1.1 Aegon: Driving Customer Engagement & Sales with Big Data 5.1.2 Aetna: Predicting & Improving Health with Big Data 5.1.3 Allianz Group: Uncovering Insurance Fraud with Big Data 5.1.4 Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data 5.1.5 AXA: Simplifying Customer Interaction with Big Data 5.1.6 China Life Insurance Company: Elevating Risk Awareness with Big Data 5.1.7 Cigna: Streamlining Health Insurance Claims with Big Data 5.1.8 Dai-ichi Life Holdings: Unlocking & Opening Doors to Life Insurance with Big Data 5.1.9 Generali Group: Digitizing the Insurance Value Chain with Big Data 5.1.10 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data 5.1.11 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data 5.1.12 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 5.1.13 Zurich Insurance Group: Improving Risk Management with Big Data 5.2 Reinsurers, InsurTech Specialists & Other Stakeholders 5.2.1 Atidot: Empowering Life Insurance with Big Data 5.2.2 Cape Analytics: Delivering Instant Property Intelligence with Big Data 5.2.3 Concirrus: Enabling Smarter Marine & Auto Insurance with Big Data 5.2.4 JMDC Corporation: Optimizing Health Insurance Premiums with Big Data 5.2.5 MetroMile: Revolutionizing Auto Insurance with Big Data 5.2.6 Munich Re: Pioneering Cyber Insurance with Big Data 5.2.7 Oscar Health: Humanizing Health Insurance with Big Data
Chapter 6: Future Roadmap & Value Chain 6.1 Future Roadmap 6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence) 6.1.2 2020 - 2025: Large-Scale Adoption of Usage & Analytics-Based Insurance 6.1.3 2025 - 2030: Towards the Digitization of Insurance Services 6.2 The Big Data Value Chain 6.2.1 Hardware Providers 6.2.1.1 Storage & Compute Infrastructure Providers 6.2.1.2 Networking Infrastructure Providers 6.2.2 Software Providers 6.2.2.1 Hadoop & Infrastructure Software Providers 6.2.2.2 SQL & NoSQL Providers 6.2.2.3 Analytic Platform & Application Software Providers 6.2.2.4 Cloud Platform Providers 6.2.3 Professional Services Providers 6.2.4 End-to-End Solution Providers 6.2.5 Insurance Industry
Chapter 7: Standardization & Regulatory Initiatives 7.1 ASF (Apache Software Foundation) 7.1.1 Management of Hadoop 7.1.2 Big Data Projects Beyond Hadoop 7.2 CSA (Cloud Security Alliance) 7.2.1 BDWG (Big Data Working Group) 7.3 CSCC (Cloud Standards Customer Council) 7.3.1 Big Data Working Group 7.4 DMG (Data Mining Group) 7.4.1 PMML (Predictive Model Markup Language) Working Group 7.4.2 PFA (Portable Format for Analytics) Working Group 7.5 IEEE (Institute of Electrical and Electronics Engineers) 7.5.1 Big Data Initiative 7.6 INCITS (InterNational Committee for Information Technology Standards) 7.6.1 Big Data Technical Committee 7.7 ISO (International Organization for Standardization) 7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 7.7.4 ISO/IEC JTC 1/WG 9: Big Data 7.7.5 Collaborations with Other ISO Work Groups 7.8 ITU (International Telecommunication Union) 7.8.1 ITU-T Y.3600: Big Data - Cloud Computing Based Requirements and Capabilities 7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 7.8.3 Other Relevant Work 7.9 Linux Foundation 7.9.1 ODPi (Open Ecosystem of Big Data) 7.10 NIST (National Institute of Standards and Technology) 7.10.1 NBD-PWG (NIST Big Data Public Working Group) 7.11 OASIS (Organization for the Advancement of Structured Information Standards) 7.11.1 Technical Committees 7.12 ODaF (Open Data Foundation) 7.12.1 Big Data Accessibility 7.13 ODCA (Open Data Center Alliance) 7.13.1 Work on Big Data 7.14 OGC (Open Geospatial Consortium) 7.14.1 Big Data DWG (Domain Working Group) 7.15 TM Forum 7.15.1 Big Data Analytics Strategic Program 7.16 TPC (Transaction Processing Performance Council) 7.16.1 TPC-BDWG (TPC Big Data Working Group) 7.17 W3C (World Wide Web Consortium) 7.17.1 Big Data Community Group 7.17.2 Open Government Community Group
Chapter 8: Market Sizing & Forecasts 8.1 Global Outlook for the Big Data in the Insurance Industry 8.2 Hardware, Software & Professional Services Segmentation 8.3 Horizontal Submarket Segmentation 8.4 Hardware Submarkets 8.4.1 Storage and Compute Infrastructure 8.4.2 Networking Infrastructure 8.5 Software Submarkets 8.5.1 Hadoop & Infrastructure Software 8.5.2 SQL 8.5.3 NoSQL 8.5.4 Analytic Platforms & Applications 8.5.5 Cloud Platforms 8.6 Professional Services Submarket 8.6.1 Professional Services 8.7 Application Area Segmentation 8.7.1 Auto Insurance 8.7.2 Property & Casualty Insurance 8.7.3 Life Insurance 8.7.4 Health Insurance 8.7.5 Multi-Line Insurance 8.7.6 Other Forms of Insurance 8.7.7 Reinsurance 8.7.8 Insurance Broking 8.8 Use Case Segmentation 8.8.1 Personalized & Targeted Marketing 8.8.2 Customer Service & Experience 8.8.3 Product Innovation & Development 8.8.4 Risk Awareness & Control 8.8.5 Policy Administration, Pricing & Underwriting 8.8.6 Claims Processing & Management 8.8.7 Fraud Detection & Prevention 8.8.8 Usage & Analytics-Based Insurance 8.8.9 Other Use Cases 8.9 Regional Outlook 8.10 Asia Pacific 8.10.1 Country Level Segmentation 8.10.2 Australia 8.10.3 China 8.10.4 India 8.10.5 Indonesia 8.10.6 Japan 8.10.7 Malaysia 8.10.8 Pakistan 8.10.9 Philippines 8.10.10 Singapore 8.10.11 South Korea 8.10.12 Taiwan 8.10.13 Thailand 8.10.14 Rest of Asia Pacific 8.11 Eastern Europe 8.11.1 Country Level Segmentation 8.11.2 Czech Republic 8.11.3 Poland 8.11.4 Russia 8.11.5 Rest of Eastern Europe 8.12 Latin & Central America 8.12.1 Country Level Segmentation 8.12.2 Argentina 8.12.3 Brazil 8.12.4 Mexico 8.12.5 Rest of Latin & Central America 8.13 Middle East & Africa 8.13.1 Country Level Segmentation 8.13.2 Israel 8.13.3 Qatar 8.13.4 Saudi Arabia 8.13.5 South Africa 8.13.6 UAE 8.13.7 Rest of the Middle East & Africa 8.14 North America 8.14.1 Country Level Segmentation 8.14.2 Canada 8.14.3 USA 8.15 Western Europe 8.15.1 Country Level Segmentation 8.15.2 Denmark 8.15.3 Finland 8.15.4 France 8.15.5 Germany 8.15.6 Italy 8.15.7 Netherlands 8.15.8 Norway 8.15.9 Spain 8.15.10 Sweden 8.15.11 UK 8.15.12 Rest of Western Europe
Chapter 10: Conclusion & Strategic Recommendations 10.1 Why is the Market Poised to Grow? 10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 10.3 Big Data is for Everyone 10.4 Evaluating the Business Value of Big Data for Insurers 10.5 Transforming Risk Management 10.6 Tackling Cyber Crime & Under-Insured Risks 10.7 Accelerating the Transition Towards Usage & Analytics-Based Insurance 10.8 Addressing Customer Expectations with Data-Driven Services 10.9 The Importance of AI (Artificial Intelligence) & Machine Learning 10.10 Impact of Blockchain on Big Data Processing 10.11 Adoption of Cloud Platforms to Address On-Premise System Limitations 10.12 Data Security & Privacy Concerns 10.13 Recommendations 10.13.1 Big Data Hardware, Software & Professional Services Providers 10.13.2 Insurance Industry Stakeholders
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