Use case: insurance
Built for cybersecurity insurance risk modeling
Policyholder’s threats are increasing.
Your insurance payouts don’t have to.
Problem:
Poor liability assessment leads to claims payouts
An insurance provider observed increasing payouts due to unforeseen claims, many related to undetected risk factors and potential fraud. This lack of predictive insight into client risk profiles resulted in higher-than-anticipated liabilities, impacting profitability and risk management.
So what’s the solution?
The insurer used RiskyTrees to build attack trees focused on uncovering hidden risk factors within policyholder profiles and identifying fraud paths in claims.
Through RiskyTrees’ predictive analysis and fraud detection templates, the team modeled high-risk behaviors, tagging patterns that could indicate future liability. This insight enabled underwriters to better assess client risk at policy inception and adjust premiums accordingly, while helping the underwriters recommend appropriate fraud monitoring tools to help prevent claims.
Outcomes of using RiskyTrees
Payouts decreased
Total payouts decreased as the company proactively adjusted premiums for higher-risk clients
Predictability improved
The improved predictability of client risk and liability allowed the company to balance premiums more effectively.
Profit margins increased
By improving the predictability of their client’s risk and better balancing premiums, expected margins increased on each client.