Predictive analytics is a fancy set of statistical techniques and algorithms that allow you to peek into the future performance of policies and select the right ones to add to your portfolio, assign the right price, and proactively manage claims.
Using predictive models to inform decision-making is table stakes in today’s competitive environment. The two most important questions are how best to use predictive modeling and what you plan to achieve as a result.
Make sure all the relevant stakeholders understand the business goals from the beginning and that you have secured executive commitment and sponsorship.
Predictive Modeling Best Practices
- Data Custody – Establish a process to standardize, normalize data.
- Data Partitions – A/B testing is not sufficient and can result in erroneous conclusions. Four partitions are recommended.
- Model Validation – Is it predictive? Accurate? Is it better than what we have now?
- Model Type – Identify how the model will be used – is it an automated use case or will human beings need to understand the results?
- Time to Market – Market conditions change. If it takes 18-24 months to deploy, it will have lost significant business value.
IT Resource Considerations
- Scope & Priority – Evaluate the scope and where this initiative fits in the queue.
- Deployment Tools – Do you have a deployment platform? Can it be incorporated into the existing workflow?
Valen’s Predictive Modeling Capabilities
Valen is a widely recognized leader in predictive modeling, and our cloud-based platform allows us to deploy solutions very quickly with minimal IT resources required.
- Custom Built – Every Valen client has a custom model built for their business needs. No two models are the same.
- Robust & Verified – Our data assets, data management and predictive modeling practices are world-class. Your predictive model will perform as promised. Valen clients see on average a 22% loss ratio improvement.
- Integrated – We have all the systems integration resources necessary to plug into your workflow in real-time for a seamless implementation.
- Intuitive – Our analytics are plainly explained and straightforward.
- Fast – Our average model build and deployment takes only 2-3 months.