Decision Science brings together artificial intelligence, business context and subject matter expertise to bridge the gap between data and the people who make decisions.
Life and health insurers are looking for ways to use the data available to them to unlock value. We believe the answer is Decision Science. Using the Decision Science Cycle and a Decision Science platform powered by AI and machine learning, insurers can predict possible outcomes of important decisions to achieve optimal decision outcomes. Once decisions have been executed, insights can be fed into future decision making. The result is simplified, repeatable high value decision making benefiting both shareholders and policyholders.
Data is transforming every process, product, and interaction for today's life and health insurers. While the importance of data is widely accepted, it doesn't mean it should be the sole basis for company strategy. Because the decisions made today are critical for future growth, it's vital life and health insurers' take a decision-driven, rather than purely data-driven, approach to long-term strategy.
Broad access to a variety of customer, competitor, and market data sources with billions of data points means there is no limit to what insurers can learn and leverage with the right tools. Focusing on the decisions life and health insurers must make, Decision Science allows insurers to explore a wide range of data-driven scenarios and potential decisions to identify the optimal choice.
With Decision Science, insurers can act and react decisively to challenges and opportunities and better align their decisions and trade-offs with long-term company objectives.
While data analytics and industry expertise are critical to Decision Science, they're not what makes this approach distinct.
The Decision Science Cycle is uniquely transformative by beginning with a business problem and focusing on the decisions that need to be made to solve it. Whether it's addressing a new opportunity, confronting an old issue, or testing an innovative new approach - the process begins with a clear problem to be solved.
The Decision Science cycle is powered by a Decision Science platform leveraging:
This unique and powerful combination lets life and health insurers analyze a wide variety of data sources within the context of a decision. Decision Science allows them to explore the possibilities, opportunities, and trade-offs of different outcomes while leveraging both data science and an actuarial perspective which is unique to the life and health industry.
"The result is actionable, accessible insights that illustrate the impact different decisions have on insurers' profitability, customer lifetime value, bottom line, growth, portfolio performance, and more."
The result is actionable, accessible insights that illustrate the impact different decisions have on insurers' profitability, customer lifetime value, bottom line, growth, portfolio performance, and more. It quite literally turns the decision making process into a scientific one, enabling insurers to explore many different scenarios to identify which aligns with product, portfolio and strategic goals. The impact of each decision then generates further data, feeding back into a repeating cycle which is constantly refined by new information.
Optimize processes like pricing, product development, underwriting, and claims management, with the specific intent of gaining actionable insights from data.
Base important decisions on validated information, ensuring they are aligned with long-term objectives.
Assess a wide range of scenarios and efficiently evaluate different decisions and their trade-offs.
Swiftly identify and react to challenges and opportunities in real time and improve damage control.
A Decision Science platform takes this data and organizes it into understandable patterns to better inform company strategy.
As the platform takes in more data, its insights become increasingly granular, enabling:
- Better targeting based on customer behavior and purchasing patterns
- Better understanding of demand elasticity
- Improved personalization across product, pricing, distribution, etc.
- Clarity on how to adjust company strategy to improve returns, profitability, retention, etc.
Granular data and feedback make processes more flexible, scalable, and sustainable over time.