The Insurance industry is no stranger to manual, resource-intensive, inaccurate, and time-consuming processes, especially when it comes to Underwriting. Over the years, this has gradually started to change with the advancement of digital technology and the need to innovate in order to compete in today's competitive markets, with automated underwriting.
Underwriting Efficiency Challenges
Manual underwriting evaluates risks using outdated business rules, IT systems, and printed documents. Often, they perform tasks in Excel that are inaccurate, error-prone and time-consuming. The evaluation process considers various factors, such as the applicant's financial history, health records, and property criteria.
As you can imagine, this resource-intensive and error-prone evaluation process creates a major backlog for teams. Delays caused by human error and slow processing time result in a loss of business opportunities and customer dissatisfaction. Also, in many cases, delays will violate SLA and cause compliance issues.
To address these challenges, insurance companies must turn to decision automation and a decision-centric approach to streamline the underwriting process.
The Role of Business Rules in Automated Underwriting
The need to process applications faster and at scale while remaining accurate led the way toward decision automation by implementing Decision Intelligence in Underwriting. Initially, this began with the use of rule engines to evaluate risk and make real-time decisions.
Using automated business rules helps in two ways:
- Reduces errors and ensures fairness by applying the business rules consistently
- Improves efficiency, with turnaround times going from months to days
The Role of Machine Learning in Automated Underwriting
Insurance underwriting is about making and executing decisions. Although many underwriting decisions are rule-driven, there are many areas in which leveraging Machine Learning will boost the outcomes.
Integrated Machine Learning as part of the Decision-Making Process enables you to create a prediction engine to identify risks.
Some of Machine Learning use cases can be scenarios such as:
- Premium Calculation
- Dynamic Pricing
- Fraud Detection
- Predicting Customer Behavior
There are many use cases in using Machine Learning that we can train an algorithm based on some historical data and create a read-to-use machine learning model.
The challenge is now to integrate those algorithms into the holistic view of the business decision i.e. Decision Graph.
Once the rules are modelled and Machine Learning model are trained, they both will be integrated into the holistic view of business decisions that is represented by a Decision Graph.
The Role of Decision Modeling in Automated Underwriting
The insurance industry is in the business to assessing risk and then making decisions and executing decisions based on the determined risks. To make sure the outcome of the decisions are accurate, consistent and transparent they should use decision modeling.
At the very minimum the decision models should be based on Decision Model and Notation (DMN) with Conformance Level 3 which makes the models executable.
Decision requirements diagram – DMN for rapid disaster recovery allowing tow insurance providers negotiate on behalf of the parties.
The purpose of DMN is to provide a model-based notation that is understandable by business analysts, operations and SMEs who can create decisions and rules with no reliance on technical developers to implement them into applications.
The value proposition of using DMN in your organization for decision-making will:
- Help stakeholders grasp complex decision-making domains through clear, easy-to-read diagrams.
- Provide a solid foundation for discussions and agreements on the scope and nature of business decisions.
- Reduce effort and mitigate risk in decision automation projects by decomposing requirements of complex business decisions.
- Allow business rules to be clearly and reliably defined in straightforward decision tables.
- Enable development of reusable decision-making libraries and components.
- Simplify development business rules and decisions in IT systems and processes with specifications that can be automatically validated and executed.
- Ensures successful integration of machine learning and predictive analytics models in decision-making scenarios
Book a Custom Demo
The Role of Orchestration in Automated Underwriting
The decision graph that includes business rules and machine learning can execute decisions.
But here is the gotcha.
Consuming the decision model requires gathering data and information from databases, third-party services, IT systems, etc. Once the data is gathered, it should be validated and shaped to the form that the decision model (i.e., Decision Graph) expects. Then, it can be passed for execution.
This is a very complex, messy, procedural, and fragile task as data keeps changing, IT systems keep updating, and system integration changes over time. Orchestration enables this in an easy and scalable manner.
Additionally, after executing the decisions, systems, processes, and databases must be updated and reflect back the results and outcomes of the decisions. This is another case for having an integrated long-running orchestration as part of your toolbox.
Conclusion
Business rules change, data points become available and insurance providers should execute decisions based on business rules, machine learning and new data points.
This is a complex task that, if not done properly, will lead to errors, inaccuracy, compliance issues, and customer dissatisfaction.
Automated Underwriting is critical for insurance organizations to ensure they can make quick, accurate, consistent, and transparent business decisions.
Insurance providers implementing automated underwriting will have:
- Faster Processing: the underwriting team can process applications faster than manual Underwriting.
- Improved Accuracy: reduces the risk of human error.
- Increased Efficiency: automates routine decisions, freeing up underwriters to focus on more complex cases.
- Better Risk Assessment: can analyze large amounts of data from portals and other data sources to identify patterns.
- Personalization at scale: enabling customer policies to be tailored to specific individual needs and circumstances.
- Better Compliance: reducing the risk of non-compliance by keeping your business decisions updated to regulatory changes.
Last updated November 3rd, 2025 at 11:13 am Published December 4th, 2024 at 03:11 pm
