Tag Archives: Wunelli

fortune telling

Fortune Telling for Insurance Industry

In the world of InsurTech, there are distribution players and there are data players. The data players are essentially doing two things:

First, they are enabling and exploiting new sources of data, such as telematics, wearables and social listening.

Second, they are processing data in completely new ways by applying data science, machine learning, artificial intelligence and high-performance computing.

The result is that, for insurers, the InsurTechs are creating opportunities for the development of new products for new customers; improved underwriting and risk management; and radically enhanced customer engagement through the claims process.

Which is why, in my humble opinion, tech-driven innovation in insurance will be data-driven.

As a result, this week I feature an Israeli start-up called Atidot, a cloud-based predictive analytics platform for actuarial and risk management…aka, the next gen of data modeling and risk assessment!

I’ve recently Skyped with CEO Dror Katzav and his co-founder Barak Bercovitz. Both have a background in the Israeli military, where they were in the technological init of the intelligence corps. Both have a background in cyber security, data science and software development.

These are two very smart cookies!

And they have applied their minds to the world of insurance and, very specifically, to data. To change the way that data is cut and diced to provide multiple insights from very different perspectives has been their purpose.

Atidot
The result is Atidot, which in Hebrew means, “fortune telling.”

What’s the problem?

Dror explained it to me:

“Insurers (or rather, actuaries) are not doing all that they could with the data they have. And there are several reasons for this.

“First, they miss the point, Insurers look at data from a statistical perspective and miss out on the insights and perspectives that can be seen from different points of view.

“Next…, the traditional modeling tools that are still being used today are cumbersome, difficult to re-model and rely heavily on manual effort. With new sources of data now available, these tools are simply inadequate to handle them.

“And third, they’re too slow. The frequency of updating the models is too long, measured in weeks and months. This is because many of the current tools are limited in scale and flexibility, unable to cater for the huge volumes of data now available to them.”

How is work done today?

Today, insurers think about key questions to ask prospective policyholders. Do you smoke? Do you drink? Do you have diabetes? What is your gender? What is your location?

Insurers map the customer’s answers onto a statistical table. This linear modeling approach provides a risk rating of a certain outcome, such as the mortality rate for a life product.

But data science does not follow a linear model. It is different and varied. Data is modeled to show different correlations of risk to key variables.

This is what Atidot does.

It applies multiple approaches simultaneously to process a much larger set of data. This will include existing data that was previously ignored, such as the day of the month the salary is paid or frequency of ATM withdrawals, through to new sources of data, such as driving behavior or activity levels.

And while it is still very new for insurers to link, for example, increased levels of activity to mortality rates, there is enough evidence to suggest that it is just a matter of time before they do. You only have to look at the number of competitions on Kaggle to see that!

This shift gets to the crux of the insurer’s problem:

Quite simply, traditional models don’t have the ability to handle the new sources of data. Nor do they have the muscle to process it.

I’ve previously covered some brilliant InsurTechs in the data space, including Quantemplate and Analyze ReFitSense is a data aggregation platform that provides insurers with a new source of data to underwrite life risk differently. The platform collects data from all major fitness and activity tracking devices. The data is then normalized (to weed out differences in the way activity is tracked) and presents the underwriter with a common score to indicate activity patterns and levels (just as Wunelli enables a driver behavior score from telematics data).

However, the challenge for insurers is knowing what to do with this data and how to handle it.

Dror put this into context for me:

“Let me give you an example from a South African life company who were building two life products – accidental disability and severe infection disease. To test our platform, we ran their traditional method alongside ours.

“We found that they had a lot of data about their customers that they were not using or taking advantage of. And even if they tried to, the actuaries did not have the means to group this data and properly assess it in their models.

“Atidot were able to group the data differently using our tech and show them how they could significantly improve the accuracy of their forecast tables.

“We showed them how they could look at data in a different way.“

This all sounded great, so I pressed Dror for examples and we started to talk about a piece of data that seemed irrelevant to a life risk assessment – the day the premium is collected.

Dror showed me a sample of data from a live pilot the company ran for a U.S. life business on a 50,000-customer sample.

It showed that customers who paid their premiums on the 14th of the month had a 20% lower lifetime value than those who paid on the 1st.

Atidot graph
By enabling multiple data models to run simultaneously and picking the best model to better understand customers, Atidot drew a relationship between data that the actuary didn’t have before. Nor would the actuary have intuitively thought of it or arrived at it through a linear modeling approach.

So, is this enough to change the way insurers rate risk? Or change the risk selection criteria for an insurer?

To answer this I turned to Alberto Chierici, co-founder of Safer and an actuarial consultant with Deloitte. He told me:

“One issue to overcome for insurers is communication to the customer and regulators. For example, in some states it is compulsory to communicate to consumers why and how rating factors (gender, age, ZIP code) are used in pricing.

“That is making many insurers reluctant to adopt machine-learning-based risk rating and pricing. Think about the example you cited about people paying the 1st of the month versus people paying the 14th – how do you explain that to customers?”

Alberto pointed me to this discussion on Kaggle to illustrate the point.

One thing is clear, the InsurTech puck is heading Atidot’s way.

 

The original version of this article appeared here.

Telematics: Because Accidents Happen

As I researched recent developments in the telematics space, I thought of the wise words of an unknown car driver: “The worst fault of a car driver is his belief that he has none.” Whenever I speak to a group on telematics, I ask the audience, “Who considers themselves to be a better than average driver?” Every time, at least 80-90% of the hands go up.

Even if we are all close to perfect drivers, accidents will still happen. And telematics data can be used to help identify who is at fault.

Claims handling might be the new frontier for telematics, in general; beyond the early adopters of telematics-based pricing, many insurers have run pilots and proofs of concept with telematics in areas such as product development, underwriting, new business and market segmentation. They have gathered insights and developed telematics-based solutions for the broader market, often with the support of increasingly sophisticated telematics solution providers in technology or data and analytics. In fact, the SMA 2015 report, “The Changing Auto Insurance Landscape: Influencers Driving Disruption and Change,” revealed that, since its introduction to the market in 2010, telematics has come to be recognized as a maturing rather than emerging technology and often gets incorporated into connected car initiatives. The study also discussed how the industry is starting to investigate even newer technologies that might affect the auto insurance market, such as shared transportation and the autonomous vehicle.

However, it would be a mistake to move on to newer technologies and initiatives without further considering investments in telematics, especially when the full business value of telematics offerings may not have been reached yet.

Right now, particularly in personal lines, telematics is used primarily for market segmentation, product and underwriting purposes. There is a growing appreciation, though, of the value of telematics in claims handling beyond accident avoidance and driver education. For example, at a recent LexisNexis/Wunelli insurance event, it was demonstrated that telematics can play a key role in claims investigations by helping to determine which party is at fault – not always a clear-cut matter. In the specific accident discussed, two cars hit each other in the parking lot of a supermarket. The physical damage did not give a clear picture of who was at fault, and the drivers disagreed in their statements about what actually happened. One of the cars involved, however, was equipped with a telematics device. At the request of the driver of this car, the insurance carrier was able to analyze the data on the location and speed of her car immediately preceding this accident. This analysis made it abundantly clear that the driver of the telematics-equipped car could not have been at fault, which provided the insurer with proof to settle the claim accordingly.

I found it even more shocking that it was the insured driver who actually had to point out to her insurer that telematics data was available and that access to that data could be of great help in handling this claim. It was obviously not standard procedure for this specific carrier to look at telematics data in the claims handling process – and in this case, without the driver’s suggestion, the opportunity would have been missed.

Unfortunately, I don’t believe that this carrier is unique. I would urge personal lines carriers, in particular, to consider the uses and applicability of telematics data outside of the market segmentation, product and underwriting functions. We can all learn from examples like this one, as well as from the commercial lines telematics applications for risk management and claims handling.

Because we all know that, even though we drive better than the average driver, accidents still do happen.