Identify and stop high-risk transactions before booking confirmation
claims rate reduction
lower user verification cost
of transactions impacted
Outdoorsy is the first peer-to-peer marketplace to disrupt the $32B recreational vehicle industry. The company connects passionate RV owners with qualified renters and makes it easy and safe for them to transact.
It has the most comprehensive platform for outdoor travelers to rent RVs in different countries. Their selection spans vintage Airstreams, toy haulers, fifth wheelers, Class A, B, and C of RVs, as well as garden variety trailers and motorhomes.
Like many other vehicle sharing marketplaces, Outdoorsy used a combination of manual reviews and motor vehicle records (MVRs) to assess the risk of their transactions. This process worked while the company was small, but they knew they needed to bring their insurance stack to the next level to support their high growth. They needed a way to reduce friction in the customer experience and reduce verification costs while identifying high-risk transactions.
They had a hypothesis that third-party vendors could help improve their risk stack when they first met Tint.
There are too many vendors that promise to help with fraud, but we didn't know one that helped us reduce our insurance costs while growing fast. That's why we decided to try Tint.
After fully exploring and understanding the problem, it made sense for Outdoorsy to try Tint Score, which is an AI-powered risk score that is customized for platforms to predict insurance claims. The technology combines multiple data streams from clients (users, transactions, messages, in-website/app events, customer support interactions, and claims) with hundreds of external attributes into comprehensive AI models.
The first step was to run a free pilot to evaluate the impact that the Tint Score would have for Outdoorsy. Tint collected historical data from Outdoorsy and augmented it with hundreds of attributes from its network of third-party vendors. This dataset was then used to train dozens of machine learning models to determine which one was the best in predicting Outdoorsy's insurance claims.
The pilot results immediately showed a reduction of at least 11% of their losses with almost no impact on revenue. Excited with what they saw, the Outdoorsy team moved forward in deploying the Tint Score in production. The Tint API was integrated on Outdoorsy's product less than one month after signing the contract and Tint Score started to power the risk assessment of all transactions booked in the marketplace.
Outdoorsy reduced its insurance claims rate by 25% since it deployed the Tint Score, while impacting only 4% of their transactions. Additionally, the cost of user verification was reduced by over 90%. Overall, the investment on the Tint Score provided a substantial ROI for the company.
The Tint Score really helped us identify the transactions with the lowest risk, which are the most profitable, and reduce the friction to them. This led to better conversion and higher overall profitability for us. We're super happy and recommend it to other marketplaces.
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