Insurance pricing model determines the amount a customer pays as a premium, resulting in a gain or a loss for the insurance company at the end. This pricing can fluctuate depending on factors such as economic fluctuation and government charges.
According to a recent survey conducted by brokerage Alera Group Inc., there will be an increase in insurance pricing in every industry as follows.
Image: Business Insurance
“The 2021 insurance marketplace promises to be a demanding one for insurance buyers”, mentions Alera Group in their report.
If the insurance companies fail to adapt and address the changes, it will result in losing customers and major losses in profit. This mandates them to make and manage pricing decisions based on numerous changing factors such as coverage, customer data, risk exposure, etc. to not only retains customers and generating revenue but to have a competitive upper hand.
And the real concern is how insurance companies can get ready to drive their pricing models in the current dynamic insurance marketplace?
This is where a better-automated insurance pricing model.
Building an Automated Insurance Pricing Model
Building an insurance pricing model is complex and time-consuming as there are several factors to be considered such as,
- Prior claims
- Policy types
- Risk profiles
Using FlexRule® Advanced Decision Management Suite, we have created a sample insurance pricing model. In this model, first, we calculate the base premium. Then we apply a discount and finally calculate the payable amount.
In this DRD (Decision Requirement Diagram) you can see the decision hierarchy. The payable amount which is the final decision is based on the two decisions, base premium, and discount.
Base premium and discount are calculated in decision tables.
Calculate the base premium in a decision table.
Calculate the discount for each category.
Furthermore, the formula used to apply the discount is store in a BoxedExpression document.
Complexity Made Simple
As we already know that the insurance pricing models can get more and more complex over time, it requires an approach to make it simple and easier to understand.
| Challenges | Solution | ||
|---|---|---|---|
| 1 | There are a lot of attributes such as customer age, prior claims in an insurance pricing model. Determining them and understanding the relationship is difficult. | Map the entire business context in a Fact Concept along with the relationships. This also makes it easier to understand what data is required to build the pricing model. |
|
| 2 | There are several complex calculations in a pricing model to get the most accurate value. | Use a separate document such as boxed expressions to define and store the calculations. This allows reusing the formulas across different business rules. | |
| 3 | Insurance underwriters and technical people who model the business rules in a software tool cannot understand the same language. | Using a separate document such as business glossary allows defining business terms along with the programming language terms. In this way, business users can use the business term to model even without having any knowledge of a programming language. | |
| 4 | There are several facts to calculate the price creating hundreds of business rules. Keeping track of all the business rules as well as changing them over time is complex and time-consuming. | Use a decision table categorizing business rules according to each decision you want to make in order to calculate the payable amount at the end. | |
| 5 | There are multiple decisions to be made prior to the final decision of the payable amount. These decisions have a hierarchy. Determining the hierarchy according to the dependencies is challenging. | A DRD (Decision Requirement Diagram) can easily determine the hierarchy of the decisions automatically, according to the dependencies. |
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Conclusion
Due to the complexity and the regular changes, insurance pricing models require a lot of attention while modeling as well as maintaining them. At the same time, the competition between the insurance companies is increasing, trying to provide the best policies for the customers while making a profit. Therefore, building an automated insurance pricing model can benefit both the insurance company as well as its customers proving accurate outcome in a timely manner.
Learn More
- How Underwriting Teams Can Rely Less on Software Development and IT Teams
- Vehicle Insurance Claim Settlement
- Updating Business Rules in Insurance Underwriting
- Dynamic Pricing
- Streamlining Business Processes
- 3 Ways to Improve Insurance Underwriting using Decision Automation
- Machine Learning in Health Insurance Premium Calculation
- Handling Compliance Challenges in Insurance
- Minimizing Health Insurance Risks
- Why Automate Insurance Pricing Model?
Last updated March 9th, 2023 at 09:39 am, Published January 14th, 2021 at 09:39 am
Product Engineer at FlexRule – She focuses on analyzing and understanding customer requirements and identifying process. She also works in the emerging areas of technology such as Decision Robotics, testing and ensuring that the product functionalities align with the customer requirements.
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