As a free benefit for participants, we would like to extend an invitation to the Amazon SageMaker workshop on Tue, Apr 24 from 2:30-4:30pm.LEARN MORE
Welcome to the Insurance vertical page! In order to make the most of the time the weekend of the event, please review our key educational materials and data sets.
Be Prepared! Start thinking through what types of data could power your business and product ideas. Often times a combination of multiple, disparate data sets can yield the most ingenious ideas and solutions!
The following videos were recording during the April 19 Panel event. You may wish to reference them in preparation of the weekend ML event.
Insurance is a contract, represented by a policy, in which an individual or entity receives financial protection or reimbursement against losses from an insurance company. The company pools clients' risks to make payments more affordable for the insured. Insurance policies are used to hedge against the risk of financial losses, both big and small, that may result from damage to the insured or her property, or from liability for damage or injury caused to a third party. The main theme would be to use new technology, ML/AI to improve decision making abilities, decrease costs, and improve the experience for consumers. There are opportunities to disrupt the sector in many ways from smartphone insurance to business insurance.
The global Insurance premiums are estimated to be $4.5 trillion. The US alone is $1.3 T.
The market as a whole is growing at 3.7%. EM and specific insurance types grow faster.
Global development raise penetration. Current penetration is only at 6.2%.
More natural disasters, higher interest rates, and lower taxes are all good for insurance.
The consumer insurance market can be segmented into Life, P&C, and Health insurance. The US insurance markets are all growing single digits and driven by the same trends. Consumers embracing technology, new potential customers, and a willingness to change will lead to growth. (e-visits, more sensors, data collection, InsurTech).
IT spend is estimated to go up by 4.7% globally in the insurance industry. Over the last 2 years IT spend in the US insurance industry has gone up ~7% Global revenue growth is about the same at 4.6% YoY, much higher in emerging areas.
Legacy winners are big and slow but all worth many billions:
P&C: State Farm, BerkShire Hathaway, Liberty Mutual, Allstate, etc.
Life: MetLife, Prudential, New York Life, Principal, Massachusetts, etc.
Health: UnitedH, Kaiser, Anthem, Aetna, Humana, Centene, etc.
Oscar Health-Humanize HealthCare
Metromile-Pay per mile
Cover- cover anything via picture
Root Insurance - insurance base on how you drive
Tomorrow Ideas (Seattle) - app for trust, will, life insurance
Bind - Health Insurance for what you need/want not everything
At-Bay- cybersecurity insurance
Insurance is too costly and not utilizing technology to manage risk.
Consumer behavior is changing in good and bad ways that can change insurance offerings and pricing methods.
Insurance industry has no price transparency
Why do we need a local insurance agent on every corner?
What isn’t insured that should be?
Insurance seems like a scam to many? How can it be helpful?
Increased # of natural disasters
Less car ownership
IoT, Sensor adoption
Longer life expectancy
Self driving cars
Rent over owning
Amenities with purchase
Increasing amounts of data
Improved measure of risk for customers
Improve insurance processes with RPA? Easier quotes
Insuring new items
Better utilize engagement and sensor data
Automate processes for happier customers
Most market share is owned by large old companies that are not innovating.
There are over 93 types of insurance. Niche markets provide many vectors to attack.
Consumers are getting smarter with their money and spending more time researching the best options.
Must have finances in order to insure
Barriers to entry
Your novel business idea should be grounded in real-world data with plausible machine-learning/analytics on top. We've compiled a collection of datasets from which to gain inspiration. Note that you are not restricted to basing your idea on the data sets below. You may discover other open source data sets that inspire your creativity or you may bring your own proprietary data sets if you wish.
Many of the datasets below are from Kaggle, Figure-Eight (Crowdflower), Data.World, etc. The advantage of these datasets is that many have been cleaned and normalized and are ready to be explored with ML and data science tools. Note that the use of these datasets is often intended for research purposes only. Be sure to read any associated license agreements to understand if there are commercial restrictions if you plan to continuing using the data after the workshop is over.
Can computer vision spot distracted drivers?
Fatal car crashes for 2015-2016
Prediction of the charges of insurance based on information given by the people
In this dataset, you are provided over a hundred variables describing attributes of life insurance applicants. The task is to predict the "Response" variable for each Id in the test set. "Response" is an ordinal measure of risk that has 8 levels.
This data set used in the CoIL 2000 Challenge contains information on customers of an insurance company. The data consists of 86 variables and includes product usage data and socio-demographic data
Misc US Census data sets