Model outperformed all
10%
Cut reduced
50%
Premium captured
34%
Loss ratio achieved
The challenge
Optimizing profitability and risk management
A major home insurer aimed to grow its policy book by strategically targeting profitable business. It wanted to predict non-catastrophic losses per household using home attributes and estimate both unrestricted and ideal premiums to gauge profitability. It also wanted to identify policies for non-renewal based on profitability and catastrophic exposure, for a balanced and sustainable portfolio.
Key challenges
Estimating premiums for profit
Identifying unprofitable policies
Predicting household losses
Balancing growth and risk
The solution
Advanced risk and profitability modeling
Data-driven modeling
Integrated claims and premium
Identified loss drivers
Predicted losses with ML
Enhanced accuracy
Utilized home data
Built ensemble models
Compared methods
Implementation approach
1
Peril-based models
Captured risk patterns
Enhanced accuracy
Applied ML techniques
2
Policy optimization
Flagged renewables
Assessed profitability
Identified key loss factors
3
Strategic targeting
Defined targets
Determined engagement
Set action timing
The impact
Significant model impact and optimization
Improved model
Outperformed previous models
Improved accuracy
Reduced loss ratio by 19%
Loss reduction
Loss ratio at 34%
Focused on profit
Reduced non-renewals
Premium optimization
Captured 50% premium
Targeted profits
Improved performance
Looking ahead
Ongoing model refinement
Optimize predictive accuracy
Broader policy insights
Expand model application across new policy types
Sustained profitability
Drive sustained growth with data-driven strategies
