Oscar: Flirting with behavioral risk based pricing strategies

Introducing data collection through emerging technologies to innovate in the heavily regulated space of insurance companies

Oscar Health is a two-year-old startup, recently valuated at $1.75 billion when it last took $32.5 million in funding from Google Capital. Oscar aims to take a confusing, heavily regulated industry and provide a transparent, predictable, and intuitive experience at the cheapest prices possible, through design, technology, and data analytics. Since volume is an important variable in the insurance industry, Oscar has not yet managed to attain considerably lower prices than its competitors. Nevertheless, what I find interesting is that they have started to experiment with emerging technologies to create efficiencies in the system.

One example of these, although regulation forbids this type of price discrimination per se, is the fact that they’re giving out free wearable fitness tracking devices by Misfit Wearables, to monitor customers’ daily number of steps and reward them with cash prices. Isn’t this a subtle form of risk based pricing? 

Digital integration in insurance companies has a huge potential to make the system more efficient as a whole, not by penalizing a sedentary lifestyle with larger payments, but by incentivizing a healthier lifestyle and preventive care practices. Leveraging crowds to source large amounts of behavioral data can lead to identifying key variables in decreasing incidence rates for different types of health problems. Once these variables are identified, insurance providers can reward the customer if he meets certain targets, which could be as simple as number of steps per day, or if you attended or not your routine check ups, or as sophisticated as a your glucose level (See: http://time.com/3758763/google-smart-contact-lens/). This type of model will allow insurance companies to be smarter about their pricing strategy, will benefit health conscious customers with lower payments, and will ideally also incentivize more people to make small changes in their lifestyle to avoid health problems. Establishing positive incentives will also attract a more health conscious population overall and will make for a more profitable business. On the other hand, proper and timely diagnosis will avoid more expensive treatments, benefitting both the insurance provider and the customer.

There are some aspects of this theory that are definitely debatable, specially regarding to privacy and security. How much information are customers willing to give to insurance companies? Are there enough regulations set up in place to make it safe for customers to provide as many details as needed to obtain significant discounts? Should there be any limitations regarding the purpose for collecting this information? What are the security policies that need to be implemented? 

The entrance of new players like Oscar in a space that has not been innovating that much in a very long time should create the need to ask all of these questions and to create the ideal environment that will foster deep transformation and growth. 


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Student comments on Oscar: Flirting with behavioral risk based pricing strategies

  1. Great post! I think Oscar is a fascinating company that I find people frequently talking about. When I first heard about Misfit from Oscar, I thought it was a great a cool idea. After reading your post and also having used Misfit and Jawbone myself, I question whether Misfit is the best device to be handing out to customers. What was the strategic rationale behind starting with Misfit compared to a variety of other health tracking products out there. Does Oscar have data proving that Misfit is the best tracking device for the specific metrics that they are concerned about? How does it compare to other competitors in the space? And how willing are customers to provide Misfit data to Oscar? As you mention in your post, there is a selection bias – if I’ve been active then of course I’d love to share my data. And what if the Misfit is providing false data and makes the borderline customers think they’ve done more exercise or burned more calories than they actually have? As other posts have mentioned, one of the biggest risks with crowdsourcing is quality control. Does Oscar have checks in place to ensure quality data? In summary, I think it’s a great idea, but I’d like to have a better understanding of the overall Oscar strategy in order to better understand how the crowd data will be used effectively and efficiently.

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