Insurance Underwriting involves carefully assessing relevant information to determine risk levels and set appropriate terms based on insurance policies. However, determining applicable rules, and updating these rules is not easy.
- Time-consuming: Updating business rules can be time-consuming, especially for complex or large-scale underwriting business rules sets.
- Regulatory compliance: In the process of updating the underwriting business rules, organizations must follow the rules and regulations set by the authorities. This will be challenging because regulations change over time, and understanding and implementing those changes to the existing ruleset can be critical. Organizations must stay up to date, correctly interpret the rules, and adapt to new compliance requirements.
- Testing and implementation: Implementing changes to the underwriting process requires thorough testing to ensure functionality, accuracy, and reliability. Developing comprehensive testing plans are vital to minimize disruptions and mitigate potential risks.
- Complexity: Underwriting business rules can be inherently complex, involving intricate logic, multiple conditions, and exceptions. Understanding the intricacies of existing rules and making changes without introducing errors or unintended consequences requires careful analysis and validation.
Due to these challenges, updating business rules in the insurance underwriting process can be biased, inconsistent, error-prone, and involve subjective judgments based on the underwriter's interpretation.
However, these challenges can be addressed by automating the underwriting process which improves the underwriting process.
Underwriting – an Example Scenario
In an insurance company, we encounter numerous business rules such as Previous Policy Cancelled, Previous Insurance Denied, Hail Damage, car value over $70K, Previous License Cancelled, and many others. Each ruleset necessitates specific actions and updates the client portal. To address this scenario effectively, we require an approach that facilitates easy updates to rulesets while ensuring consistent and accurate results.
Modeling Underwriting Business Rules
Here are three different ways to model business which each are based on personal preference and nature of the business rules
Decision Table
The decision table allows the business rules be structured based on input and outputs.
The primary approach is a standard decision table with conditions for each business rule. By utilizing this decision table, we can easily define and understand the underwriting business rules by covering all possible combinations and having a condition for each scenario. In this Decision Table, we have defined separate business terms for each condition in a Business Glossary.
However, a challenge with this approach arises when dealing with increasing business rulesets. For a scenario implemented in the following Decision table, modifying the business rulesets becomes challenging due to the structure of the decision table.
Sometimes it when rules do not share lots of conditions, the Decision table becomes wide. To address that we can use Vertical Decision Table.
The vertical decision table is implementing the same logic differently by make each row composition business rules using terms and operations. In this approach, we use a Decision Table to build the expressions in each row.
In this technique, we separate the expression building into 3 steps:
- data field,
- operator, and
- value
The business rule expression will be constructed in each row rather than spread across columns.
This offers enhanced readability when conditions are not shared between rules. Moreover, modifying business rulesets is notably more straightforward than the previous approach with the growing rule set.
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Natural Language
Another technique for business rules modeling is the Natural Language approach, which offers an alternative method for formulating business rules and logic based on business glossaries and terminologies.
This approach lets you define your common language using a business glossary and fact concepts to guide rule creation. The Natural Language approach is highly effective for expressing business rules. Its ability to generate executable and testable business rules has gained popularity. By employing our advanced Domain Specific Language, you can utilize the domain-specific language of your industry (such as insurance or healthcare) to write rules efficiently and accurately.
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Transparency and Traceability
Ensuring transparency and clear explainability in decision-making builds trust, and enhances customer satisfaction. This allows for a detailed view and informs precisely what happens behind each step of the decision-making process.
Another advantage of our approach is minimizing the likelihood of mistakes by giving the ability to create test cases for users. We can cross-check the updated business rules against the automated testcases.
Conclusion
Updating underwriting business rules can be problematic if the business rules are not automated to hard-coded inside the application, API layer, business processes and so on. As the decisions related to underwriting should be updated as regularly the need to update applications and processes become more evident as business rules are inside the systems.
Automating underwiring business rules enable insurance providers to apply and update business without reliance on IT and software development team for operationalization. But also allows them to test, debug and change business rules with ease and transparency.
Last updated February 17th, 2026 at 11:44 am Published June 1st, 2023 at 02:50 pm
