At the #D1Conf in Cancun last fall, Etherisc’s Renat Khasanshyn and Protofire’s Alexander Sologub gave attendees insights on how machine learning can be applied to decentralized insurance.

“The promise of decentralization is very clear to us. It has the potential to deliver insurance with an alignment of interests between the insured and the insurance company, something that is provably fair.” — Renat Khasanshyn, Etherisc

Decentralized insurance defined and articulated

As blockchain technologies such as Ethereum and decentralization strategies are both becoming more present in several industries, the time is ripe for the concept of decentralized insurance to gain traction as a fair option to both policyholders and providers. During their presentation, Renat and Alex noted many potential improvements that decentralized insurance can bring to customers:

Accelerated time to market driven by open-source collaboration

Global competition and product diversity

Cheaper premiums with optimal rate making

Additionally, deregulation goes hand-in-hand with decentralization, according to Renat, who said, “The regulation of insurance essentially makes the insurance equal to a government-sanctioned monopoly.” The decentralized approach, in comparison, offers a previously unknown transparency to policyholders, and thus “anyone can inspect the smart contract that provides the service,” he said, “and a lot of the things that happen in the back end are all open-source.”

Enter Machine Learning

Ratemaking, fraud detection, and loss adjustment are critical in decentralized insurance (as with traditional insurance) and here’s where machine learning enters the picture.

“We believe that different drivers for decentralized insurance require qualities that machine learning brings to the table.” — Renat Khasanshyn, Etherisc

Previously expensive and viable only to organizations with very large resources, machine learning has become more affordable recently. Renat and Alex cited the use of TensorFlow (a technology that came out of Google), not only for its open-source DNA but its mobile-first and connectionless approach as well. “It’s built for a mobile-first world,” said Renat. “The products and the models built with TensorFlow work on mobile devices even without the Internet.”

TensorFlow requires data sets to be able to train models, as with all ML environments, and there are already many pre-trained models available for it. Furthermore, pre-trained models can be used without having access to large amounts of data.

“You only need a small set of data. Even a hundred or a few hundred images is enough to get the first results,” said Renat. “Traditionally, you couldn’t do much without huge gigantic data sets.”

Inception-v3, for example is a machine learning model typically used for image classification. Using TensorFlow, the model can be configured for use in fraud detection.

“Time to market is greatly reduced by having the choice between different libraries and models that you can choose to test and see if it works with your data set.” — Renat Khasanshyn

Training models for insurance

Alex picked up the discussion by noting that, historically In the world of insurance, actuaries use different computation models for calculating various costs. For instance, the Tweedie generalized linear model (GLM), has been used for decades to compute for pure premiums. According to Alex, TensorFlow can be trained using Tweedie GLM or any other model that can be used for insurance. Each model fits with different criteria, so “it’s not about finding the true model under the data. It’s about building several models and choosing the best one.”

“We can’t predict the future, but we can find some kind of underlying dependency.” — Alexander Sologub, Protofire

Early days

Yet even with the sudden surge of interest in blockchain, decentralized insurance applications are still relatively new to the market with the first instances being offered last year. As with any other service, success in the insurance business is contingent upon actual service delivery, competitive pricing, and any edge that can be gotten. With ever more providers likely to show up this year in the nascent decentralized insurance business, machine learning may be that edge, that competitive advantage.

With this in mind, Alex provided a demonstration of a working pure premium prediction model as the highlight of this talk. You can check it out in the video below.

Elements of decentralized insurance (1:35) Use cases for machine learning (2:50) Why TensorFlow? (4:55) Using pre-trained models (6:50) How are premiums calculated? (11:20) Generalized linear model for insurance (16:20) Pure premium prediction model (19:45)

The slides used in the presentation can be found below as well.