After thoroughly exploring and understanding the company’s challenge, Outdoorsy integrated with Tint’s AI-powered risk score, which was customized for its platform to predict the likelihood of future insurance claims. The technology combines multiple client data streams (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 pilot program to evaluate the impact that integration with Tint would have on 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 was best at predicting Outdoorsy's insurance risks. The winning model immediately showed a reduction of at least 11% of their losses with almost no impact on revenue.
Excited by the results, the Outdoorsy team took action and deployed Tint’s models in production. The Tint API was integrated into Outdoorsy's product less than one month after signing the contract and started to power the risk assessment of all transactions booked in the marketplace.
