1.

It starts by identifying the business problem and the decisions to be made. Some examples of problems to focus on include:

• How should we set rates, fees, and discounts to have a positive impact on sales, claims, and cross-sell?
• How can we improve broker performance through pricing, product, and commission decisions?
• What types of intervention can be made to positively impact customer behavior?
• What claim interventions will impact customer and claim behavior in a way that’s best aligned with my goals?
• Which features should our products include in order to positively impact addressable market and sales?

2.

We work with the insurer and subject matter experts, bringing a broad view of subject matter expertise and third party data to the table, We'll be asking ‘what if’ and modeling a wide range of scenarios to maximize value from Decision Science. 

3.

Ingest data. Working with insurers’ internal teams and key decision makers, we bring data into our Decision Science platform. With our powerful AI technology and ML algorithms, we use predictive and propensity models to model scenarios and experiment with different factors, determining potential outcomes based on specific parameters and constraints.

4.

We analyze the results. Based on an insurers’ experience and objectives, we take an actuarial lens and explore how business expectations line up against the results from the data. These results drive further questions and strategic thinking.

• How do we adjust expectations?
• Are there additional factors we can consider to expand our options?
• What are the potential trade-offs?

5.

We identify the optimal action. With all the information, we consider the opportunities or challenges with regard to this decision. We work with decision makers, ensuring they gain a clear view of their options and how best to exploit them.

6.

We help implement the decision. Once an insurer has all of the information, the decision comes to life. The results generated in the market are monitored vs. expected outcomes and insights are turned into more data, feeding back into the Decision Science Cycle and then....

7.

We repeat. The decision making process is a never ending cycle, and our Decision Science platform is designed to reflect that, improving the granularity of its insights over time. Insurers can explore all the opportunities available within every major decision, knowing that, when it’s time to implement, they have all the information required to make the best decision possible.