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Shared mobility’s risky business

Rachel Allen · April 18, 2018 · 3 min read
Originally posted on LinkedIn, Rachel Allen discusses why shared mobility companies need to face risk head on.

Originally posted on LinkedIn

Shared mobility has lofty growth projections, with industry experts estimating that 30% of all miles traveled will be shared by 2030 [1]. The path to 30% seems evident, as daily news highlights shared mobility platforms that focus on delivering safe, cost-effective and convenient transportation solutions. However, the state of shared miles only reaches about 1% today [2].

So, how will the industry achieve exponential growth? By providing consumers with more access to shared transportation. And how does that happen? Well, shared mobility businesses need to grow. Seems obvious, right? What’s not obvious is that the key to profitable growth lies in analytics that allow them to pull risk levers. Risk levers that help them scale access to a sprawling global landscape with varying consumer demands. Risk levers that help them manage insurance. Risk levers that help them balance cost and profit centers.

And, while risk levers are surely being used to train how autonomous vehicles make decisions, their even more eminent application is to help shared mobility platforms make decisions about drivers. Here’s just one example:

Taking a proactive approach with upfront risk management

Shared mobility operators can take advantage of assessing risk upfront, before onboarding even begins, to benefit the company’s bottom line and allow for reinvestment in growth initiatives.

Let’s game this out a bit with a quick example. Imagine you run a car share program with 100,000 drivers and vehicles already on the platform. By using the right predictive model to assess risk levels upfront (full disclosure, we built a great model at Arity to do just this), you open up a few different opportunities with the potential to A) reduce vehicle losses and B) build reserves through deposits. 

A)  Reduce vehicle losses

Applying key correlations from this model [3], you could reduce approximately 6.5% of loss costs per year by removing the riskiest 2% of drivers from the pool. If your current losses are $500/vehicle, this immediately opens up opportunities.

Original Losses/Year: 100,000 vehicles x $500 in losses/year = $50,000,000.00

Reduced Losses/Year: 100,000 vehicles x $467.50 in losses/year = $46,750,000.00

Savings in Annual Losses: $3,250,000.00 

B)  Build reserves through deposits

Now, let’s say that you need to retain the size of your driver pool, and you also want to offset the risk of keeping a larger pool. Since you know how to maneuver the lever of managing risk upfront, you can design a dynamic deposit based on subsets of your driving pool’s potential risk.

Deposit Structure: $500 deposit required from the riskiest 10%

$500 deposit x 10,000 drivers = $5MM to offset potential losses of your riskiest drivers.

Through either approach, or even both, you can take advantage of upfront risk assessment to build reserves, allowing you to reallocate funds to attract new drivers and increase your market share.

Moreover, understanding the risk of specific subsets of your driving pool lets you proactively manage the trade off between the value and losses related to those specific drivers. If the riskiest drivers are also the highest-earning drivers, then you need more detailed metrics to determine their lifetime value in order to support the long-term goals of your business. This brings up the importance of another risk lever, using ongoing risk and driving data. But, more to come on that next time.

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[3] Based on the Arity PreQual(SM) model which is trained on data from Allstate Insurance. This graph represents the correlation between losses and only the riskiest 10% of the population.

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