What is Decision Science?

How-Does-Decision-Science-Benefit-
Life-and-Health-Insurers-and-their-Policyholders
Decision Science brings together artificial intelligence, business context and subject matter expertise to bridge the gap between data and the people who make decisions.

Data is transforming every process, product, and interaction for today's life and health insurers, and the decisions made today are critical for future growth.

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. 

How Does Decision Science Benefit Life and Health Insurers and their Policyholders?

While data analytics and industry expertise are critical to Decision Science, they're not what makes this approach distinct.

The Decision Science process is uniquely transformative by beginning with an objective and the decisions executives make to bring it to life. Whether it’s addressing a new opportunity, confronting an old problem, or testing an innovative new approach - the process begins with a clear objective and its accompanying decisions.

The Decision Science Cycle

The Decision Science Cycle

The Decision Science cycle is powered by a platform leveraging:

  • Actuarial science
  • Data science
  • Artificial intelligence (AI)
  • Machine learning (ML)
  • Cloud scalability

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.

Direct Impacts of Decision Science

Better alignment between business processes and decision making
Optimize processes like pricing, product development, underwriting, etc., with the specific intent of gaining actionable insights from data. 

Plug knowledge gaps
Base important decisions on validated information, ensuring they are aligned with long-term objectives.

Evaluate broad ranges of potential decisions
Assess a wide range of scenarios and efficiently evaluate different decisions and their trade-offs.

Monthly or weekly monitoring makes course correcting simple
Swiftly identify and react to challenges and opportunities in real time and improve damage control.

Better understand customer, advisor, and competitor behavior and how they impact competitiveness and market position.
A Decision Science platform takes this data and organizes it into understandable patterns to better inform company strategy.

Improved results over time thanks to embedded AI and ML technologies.
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.
- More agile business processes become more agile, with granular data making processes more flexible, scalable, and sustainable

Decsion Science with Montoux

So where does Montoux’s Decision Science platform enter into the decision making process? Here’s how we play a role across the decision making process.

We begin with a specific objective and the decisions attached
1.

We begin with a specific objective and the decisions attached. This could be anything, including:
- 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 experts inside and outside the insurance industry, 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.

We bring in the 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 build and reuse predictive and propensity models to model scenarios and experiment with different factors, determining potential outcomes based on specific parameters and constraints.

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
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 regards 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 the information, the decision comes to life. The results this generates in the market or internally turns into more data, feeding back into the Decision Science process 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.

Decision Science in action

Montoux worked with a leading life and health insurance carrier with a large Medicare Supplement portfolio to help determine whether it could drive profitable growth by reducing the price of Medicare Supplement, a low margin, highly regulated, and homogenous product.

Could the carrier make up the loss in premiums resulting from a more competitively priced product by increasing sales and cross sales as well as improving customer lifetime value (CLV)?

The question was: could the carrier make up the loss in premiums resulting from a more competitively priced product by increasing sales and cross sales as well as improving customer lifetime value (CLV)?

Montoux worked with the customer to build an integrated CLV model comprising four underlying propensity models. By applying a CLV lens, rather than a siloed product line lens, we helped the customer identify a more competitive pricing strategy, delivering a 27% increase in sales and a 5% uplift in CLV.

27% increase in sales and a 5% uplift in CLV

Applying Decision Science also improved the customer’s business knowledge and insights by identifying and leveraging the correlation between price competitiveness and claims as well as drivers for CLV.

You can view more case studies here.

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