The True Potential of Machine Learning in the Health Insurance Industry
Health insurance premium calculation is a complex process with multiple steps. Although the major facts are age, family status, and income, to provide a more customized, accurate premium, there are several elements that require to be considered such as timely discounts and situational additional costs. Therefore, let’s discuss the challenges in calculating the premium and how they can be solved by using Machine Learning in health insurance.
Apart from the cost, fraud, and efficiency, there are five major issues in health insurance.
- Developing actionable insights from customer data
- Managing customer interactions
- Accessing external consumer information
- Communicating complex messages
- Being accountable to customers
As you can see the challenges are both data and rule-based. For instance, developing actionable insights from customer data requires machine learning to develop insights and a proper set of business rules to make the insights actionable.
However, when we use Machine Learning in health insurance, still it requires many adjustments based on changing situations. How COVID challenges the health insurance industry’s crisis management aspect is a great example of this. It required keeping the base pricing unchanged, but it also required adding an additional cost to the base premium with the new COVID situation. It proves that we need a solution using a decision graph to combine predictive (ML) and prescriptive (business rules) analytics to adjust the premium depending on the situation.
Amongst the core elements required in a Machine Learning strategy in the insurance industry, change management and data capabilities are critical to its successful implementation.
Now let’s see our solution.
Application of Machine Learning in Health Insurance Premium
The following example shows how to calculate the health insurance premium. We used the FlexRule platform to build the solution.
As shown in the following Decision Requirement Diagram (DRD), we have used both rule-based and data-driven decisioning to calculate the base premium that can be kept untouched upon situational changes. The situational decisions are added separately making it easier to add or remove, as necessary.
We used AutoML Builder to automatically train and build the decision model to predict the insurance charges based on customer’s personal data. Then we added it as a part of the above main DRD.
We used a Decision Table to define the extra charges which are added to the base premium.
A decision table was also used to define the business rules. The following decision table shows how we can add an additional cost in a COVID situation. This can be easily changed.
The Advantages of Combining Predictive and Prescriptive Analytics for Insurers
Using predictive analytics improves accuracy and efficiency. However, it also requires rule-based regulations to make it unbiased and more situation-aware. For example, in the solution we discussed, it is challenging to create business rules to determine the premium based on age, and area customers live in. Therefore, we used machine learning for that. However, the cover type such as hospitals, extras, and ambulance can have a fixed price making it suitable for rule-based decisioning. Having the situation-aware decisions separated from all these base premium calculations makes it even more manageable upon situations.
There are three major benefits of combining these predictive and prescriptive analytics:
- Adaptiveness to Changes
Provides more detailed and timely insights on existing healthcare policyholders which helps to make better decisions on potential customers. Being able to make changes easily makes it easier to adapt according to new business opportunities.
- Gross Margin Management
It helps to identify growth opportunities that will be useful to manage the gross margin. As the solution provides more transparency over decisions, you can compare and update the system as necessary. For example, due to COVID, when people lose jobs and the number of ESI (Employer-Sponsored Health Insurance) dops, you can clearly see which part of the solution should be adjusted to get a better Gross Margin (GM).
- Expense Reduction
Reduces the cost of decision-making by a person. It also reduces the cost of making inaccurate decisions. Especially, in situations like COVID, it is easier to adjust the solution with less resources reducing the cost of change management.
The health insurance market is already becoming more and more competitive. In this, calculating the health insurance premium is a challenging and yet critical process as the customers will compare the prices before buying an insurance policy. Most importantly, adjusting the premium based on different situations in a timely manner is even more challenging. To overcome this challenge, we can build a solution that uses the combined power of predictive and prescriptive analytics. It helps to combine the decisions made using the existing customer data for unknown scenarios and business rules on known scenarios. In the end, a customized, unbiased, and fair value can be calculated making both customers and the insurer satisfied.
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Last updated November 23rd, 2021 at 01:24 pm, Published June 9th, 2021 at 01:24 pm