How Riders Share increased conversion of their most profitable transactions and reduced its claims frequency by 38% with the Tint Score

Riders Share is a peer-to-peer marketplace focused exclusively on street-legal motorcycle rentals.

Riders Share

Riders Share is a peer-to-peer marketplace focused exclusively on street-legal motorcycle rentals. It was the first motorcycle sharing website in the U.S., and first to figure out insurance, which is a key part of the experience. It launched in August 2016 and is currently available nationwide.

The company is out to solve a large problem: motorcycles are ridden less than 2,000 miles per year, compared to 12,000 for cars. At the same time, there are about 30 million licensed riders and just under 10 million motorcycles in the United States. There are millions who would occasionally ride motorcycles if it wasn't for the steep prices of motorcycle rentals.

The Riders Share team knew that insurance was critical to its success. From the early days, they pioneered their insurance policy and built their risk management muscles.

Identifying the risk of their transactions would be critical to run an efficient risk stack and to have a profitable business. As a small team, they didn't have the resources to build an end-to-end data and machine learning infrastructure to optimize their risk management, so they were looking for partners.

When they met Tint, it was love at first sight.

Tint understands marketplaces, risk, and machine learning. This is a killing combination for growing marketplaces like ours. It allowed us to get on parity with the giants in the sector and use AI much faster than we'd have done internally.
Guillermo Cornejo, Founder & CEO
Riders Share is a peer-to-peer marketplace focused exclusively on street-legal motorcycle rentals.

The Solution

Riders Share and Tint decided to run a proof of concept of the Tint Score, which is an AI-powered risk score that is customized for platforms to predict insurance claims by blending 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.

Tint collected historical data from Riders Share and augmented it with hundreds of attributes from its network of third-party vendors. From there, it's technology trained machine learning models and reported the results to Riders Share.

The pilot results immediately showed that the company could identify 3 clear risk groups based on driver, motorcycle, and contextual information. This way, Riders Share would know the risk of any transaction in real-time and could adjust the friction accordingly to maximize the conversion of low-risk transactions while reducing the claims rate.

After very encouraging pilot results, the company decided to use the Tint Score to provide risk-based packages (trip fee, deductibles, deposit) for each transaction. It integrated the Tint API in its product workflows accessing the Tint Scores before displaying the transaction details to the user.


Since deploying Tint, Riders Share reduced its claims frequency (in 1000's of rental days) by 38%. The net loss per rental day (in dollars) was reduced by 66%. Finally, the conversion rate of transactions in the low-risk group was 100% higher than the high-risk group successfully driving more low-risk transactions down the funnel and pushing away higher risk transactions.

Claims frequency reduction
Losses per day reduction
Conversion difference between low and high-risk groups
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.
Guillermo Cornejo, Founder & CEO