Artificial Intelligence in Insurance - Thematic Research
Machine learning is an artificial intelligence (AI) technology which allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures.
There are many other AI technologies - from image recognition to natural language processing, gesture control, context awareness and predictive APIs - but machine learning is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.
Many companies, like Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditized by being open sourced and pre-packaged into developer toolkits that anyone can use.
This report focuses on Artificial Intelligence in Insurance.
This report is part of our ecosystem of thematic investment research reports, supported by our “thematic engine”. About our Thematic Research Ecosystem -
- GlobalData has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry. Whilst most investment research is underpinned by backwards looking company valuation models, GlobalData’s thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats. To do this, GlobalData tracks the performance of the top 600 technology, media and telecom stocks against the 50 most important themes driving their earnings, generating 30,000 thematic scores. The algorithms in GlobalData’s “thematic engine” help to clearly identify the winners and losers within the TMT sector. Our 600 TMT stocks are categorised into 18 sectors. Each sector scorecard has a thematic screen, a risk screen and a valuation screen. Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard. This report is a Multi-Theme report, covering all stocks, all sectors and all themes, giving readers a strong sense of how everything fits together and how conflicting themes might interact with one another.
Reasons to buy
- Our thematic investment research product, supported by our thematic engine, is aimed at senior (C-Suite) executives in the corporate world as well as institutional investors.
- Corporations: Helps CEOs in all industries understand the disruptive threats to their competitive landscape
- Investors: Helps fund managers focus their time on the most interesting investment opportunities in global TMT.
- Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.
Companies Discussed are -
Table of Contents
Technology trends 5
Macro-economic trends 7
Applications of AI in Insurance 8
VALUE CHAIN 14
Ten categories of AI software 15
INDUSTRY ANALYSIS 23
The tech sectorÂ’s angle 23
The Web-Scale companies 23
Enterprise software players 24
Proprietary datasets are also important 24
AI and ML are transforming the chipset market 25
The two critical components of any successful AI engine 25
IMPACT OF AI ON INSURANCE 31
Insurance case studies 35
Recommendations for IT vendors 36
COMPANIES SECTION 37
Listed tech companies 37
Privately held tech companies 40
Insurance companies 43
TECHNOLOGY BRIEFING 46
History of machine learning 46
How does deep learning work? 46
APPENDIX: OUR Â“THEMATICÂ” RESEARCH METHODOLOGY 49
Secondary Research Information is collected from a number of publicly available as well as paid databases. Public sources involve publications by different associations and governments, annual reports and statements of companies, white papers and research publications by recognized industry experts and renowned academia etc. Paid data sources include third party authentic industry databases.
Once data collection is done through secondary research, primary interviews are conducted with different stakeholders across the value chain like manufacturers, distributors, ingredient/input suppliers, end customers and other key opinion leaders of the industry. Primary research is used both to validate the data points obtained from secondary research and to fill in the data gaps after secondary research.
The market engineering phase involves analyzing the data collected, market breakdown and forecasting. Macroeconomic indicators and bottom-up and top-down approaches are used to arrive at a complete set of data points that give way to valuable qualitative and quantitative insights. Each data point is verified by the process of data triangulation to validate the numbers and arrive at close estimates.
The market engineered data is verified and validated by a number of experts, both in-house and external.
REPORT WRITING/ PRESENTATION
After the data is curated by the mentioned highly sophisticated process, the analysts begin to write the report. Garnering insights from data and forecasts, insights are drawn to visualize the entire ecosystem in a single report.