This is the fourth in our AI in Fintech Week series. You can see the intro post describing the current state of the art in AI here. Today we look at a job that very few people understand. It is a job that requires an aptitude for math and statistics plus knowledge of complex domains such as life expectancy, healthcare, accidents, weather, wars & terrorism. Fundamentally it is a job that requires math and statistics; our AI friend Hal is heard to say, “I am good at math and statistics, give us a job”. It is possible that the flood of new real time data into Insurance will change the Actuary job beyond recognition or even replace it with AI machines. That is the theory that we explore in this research note.
The Actuary job
Actuaries do statistical modeling to assess risk. However unlike using statistical modeling to assess financial risk, the job is not open to anybody with skills in math, statistics and computers. The Actuary job is a profession with rigorous professional certifications.
This site has the facts.
- It requires a big investment in education. “It could take from 6-10 years to pass all of the exams, but you can begin a career as an actuary by passing the first two exams, and then taking subsequent exams while working as an actuarial assistant.”
- It is a well-paid job. “Experienced Fellows have the potential to earn from $150,000 to $250,000 annually, and many actuaries earn more than that.”
- There are not that many Actuaries. Estimates vary but the number is around 10,000 in the USA. An upper bound would be 3x globally ie about 30,000. That is small compared to other professions/jobs.
Data is the game-changer
There four main domains for Actuaries to model
- Property & Casualty for Cars.
- Property & Casualty for Houses.
- Life Insurance.
- Health Insurance
The data points, and models are quite different in these 4 domains, but data is the key to all of them and that is where disruption is coming from. The big game-changer in Insurance is the data flowing from new data sources such as telematics in home and in car for Property & Casualty and wearables on the body for Life and Health.
One company to watch is Atidot, which is a classic Israeli startup (founders ex military intelligence, steeped in cyber security, data science and software development).
Atitdot are moving the actuarial process from classic statistical analysis using static models to predictive analytics using dynamic and non-linear models. The real world works on real time, dynamic models. Reality does not obey the dictates of a static model. The problem is that our organizational processes are based on static models.
When will this person die?
That is a classic question that actuaries need to answer, specific to Life. In Health, the question might be “what is the chance of this person getting diabetes?” In Cars it might be ““what is the chance of this person having an accident?” Lets stick with the Life question as it is the simplest. A simple statistical model might have the following variables:
- Health risks such as tobacco, alcohol and medical diseases such as diabetes.
- Average life expectancy
Occasionally the model is changed because of something global changes such as average life expectancy. Sometimes you can refine based on changes in risk profile of a job or a location. These changes affect so much in the Insurance business that they need to be carefully considered and go through committees and long established approval workflows.
That is a description of the Static Batch Enterprise, but the world is dynamic and real time.
Atidot applies many more variables simultaneously to process much larger data sets. The new data sources – telematics, wearables, social media, weather, news – are inherently real time and dynamic.
Some other ventures in this space worth tracking are:
Quantemplate focused on Reinsurance
Analyze Re focused on Reinsurance
FitSense focused on wearables data for Life & Health.
Wunelli focused on in-car telematics data for car insurance
Other AI Use Cases in Insurance
There are many possible use cases for AI for Insurance. Many of the use cases cited are really Big Data with some relatively simple algos. These are valuable but do not fall into the reasoning machines that we call AI. There are use cases where a robot replies via SMS to a custom query. These are real AI and they can to pass the Turing test. However they are not specific to insurance and can be applied to all CRM and Support use cases in any industry.
A huge pain point specific to Insurance is changing models related to weather due to climate change.
The company to track here is Meteo Protect.
AI can be used in claims processing to detect fraudulent claims. This is where human experience can detect patterns.
Replace or Augment?
Our thesis is that Actuaries will be augmented rather than replaced. AI machines will do the work that Actuaries do today. However human Actuaries will move up the value chain for two reasons:
- The Actuary profession is full of smart, well-educated people and many of them are in positions of power. They will adapt to change.
- There is far more value to an Insurance company from accuracy than cost-cutting. An experienced Actuary empowered by real time data and non-linear models will be hugely valuable. The Actuary defines how much to charge for a Premium and that is as core to an Insurance company as you can get.
In some jurisdictions, Insurance companies have to communicate to consumers how rating factors such as gender, age, zip code are used in pricing. That privacy issue will get more complex as we get into in-car telematics, smart home sensors and wearables. It is hard to imagine an Insurance company being allowed to do surge pricing like the transportation business just because the AI machine tells the Insurance company that risk has spiked.
This will be an issue that regulators will have to grapple with as InsurTech startups and Insurance incumbents battle for customers.