Common Business Rules Examples

January 4, 2024

Common Business Rules Examples

Business rules, pivotal in defining organizational activities, are more than mere guidelines; they are the backbone of efficiency and strategic operations. This section clarifies what business rules are, distinguishing them from adjacent concepts like business logic and business requirements. 

For example, while business logic dictates the overall workflow, business rules provide specific, actionable directives. 

In this article, we delve into examples of business rules, illustrating their practicality and necessity in different business scenarios. This exploration into the nature and scope of business rules sets the foundation for their deeper examination in subsequent sections.

What Are Business Rules? The definition

Ronald G. Ross, known as the "father of business rules," describes them as criteria used to make decisions in day-to-day business operations. These rules are highly specific and form the core of decision-making processes​​. They relate to data quality in two fundamental ways: automating daily operational decisions and auditing data for compliance with regulations and internal policies, leading to significant improvements in data quality.

Business rules, managed through Business Rules Management Systems (BRMS), provide a structured approach to handling operational decisions, demonstrating their utility in areas like pricing strategies, credit assignments, fraud detection, and more.

​The importance of business rules is highlighted in their role in guiding organizational behavior, shaping judgments, and driving decision-making. For instance, companies often have business rules embedded in their application code, policies, or maintained through employee knowledge. However, undocumented or "tribal knowledge" business rules risk being lost with staff attrition, underscoring the importance of formal documentation and management systems.

Statistics further emphasize the importance of business rule automation in organizational processes. According to McKinsey, 31% of businesses had fully automated at least one function as of 2020, with 66% piloting solutions to automate at least one business process. The tasks companies tend to automate first include routine processes like routing customer queries and purchase orders​.

Examples of Common Business Rules

In the realm of organizational management, the application of business rules can dramatically streamline operations and enhance decision-making in a variety of ways. Each business rule, whether it's a simple directive like routing a document to the right department based on specific criteria or a more complex regulation concerning a history of heart disease in health insurance, serves as an essential building block in the efficient functioning of a company and can help to improve customer experience. 

This section will delve into various examples of common business rules, illustrating how an example of a well-structured rule can lead to more effective and streamlined business processes. We will explore how certain types of business rules can be applied across different scenarios, from ensuring compliance and accuracy to facilitating quick and effective responses within an organization.

Dynamic pricing

Traditional rule-based engines in actuarial science primarily depend on past claims and expense data to compute premiums. However, this approach often lacks flexibility and doesn't account for the diverse factors influencing pricing decisions​​.

Dynamic pricing is a business rule that can make important, real-time changes in a matter of minutes. Here are the benefits of dynamic pricing in insurance industry:

  • Enhanced Precision and Adaptability:
  • Business rules engines introduce precision and adaptability into dynamic pricing models. For example, Higson, aids in improving product pricing and client rating calculations in a transparent environment, allowing for more tailored and flexible pricing strategies​​.
  • Such engines enable insurers to configure behavior, pricing, and risk levels of products easily and quickly, adapting to market changes and client needs​​​​.
  • Comprehensive Data Integration for Pricing:
  • Dynamic pricing models now integrate various data points, including customer behavior patterns, demographics, agent performance, and competitor outlook, providing a more comprehensive view for pricing decisions​​.
  • This holistic approach contrasts with traditional actuarial pricing, which is primarily cost-driven and relies on historical claims and expenses data​​.
  • Customer-Centric Pricing Solutions:
  • Modern dynamic pricing solutions offer optimally modified premiums suited to each customer or segment. These models consider myriad data points to maximize customer retention and insurer profitability while accounting for real-time constraints​​.
  • For instance, in group life insurance, data from operations, sales, marketing, and underwriting are integrated to build models for customer retention and conversion, using algorithms like Generalized Linear Models (GLM) and Naïve Bayes​​.
  • Real-Time Adjustments and Market Responsiveness:
  • Business rules engines allow for real-time adjustments in pricing strategies, enhancing market responsiveness. Insurers can tweak crucial risk and coverage parameters on the fly, aiming for maximal profits and reduced customer churn​​.
  • These engines also facilitate the automation of quote delivery, reducing manual processes and errors, and enabling real-time processing of complex dynamic pricing requests​​​​​​.

BREs in Insurance Fraud protection

Insurance fraud poses a substantial financial threat to the insurance industry. It is a deliberate deception against or by insurance companies or agents for financial gain and can occur at various points in the insurance transaction. Common frauds include inflating claims, misrepresenting facts on applications, submitting claims for non-existent injuries or damages, and staging accidents. This form of fraud costs the U.S. insurance industry an estimated $308.6 billion annually, highlighting the critical need for effective fraud protection mechanisms​​.

Traditional business rules systems are commonly used in insurance companies to combat fraud. These rules-based systems primarily focus on identifying obvious fraud patterns using a binary logic approach. However, they are generally less effective at uncovering new or emerging fraud schemes, making existing fraud programs vulnerable to less obvious types of fraud​​.

To enhance fraud detection, insurers are increasingly turning to advanced technologies such as machine learning models, either as standalone solutions or in conjunction with existing rules-based systems. Machine learning models require minimal human involvement, as they automatically learn from both old and new data, providing a more sophisticated approach to fraud detection.

Customer segmentation with Business Rules Engines

Customer segmentation in the insurance industry involves categorizing customers based on specific characteristics. Traditional segmentation methods have relied on demographic information such as age, gender, and behavior. However, this approach often leads to overly broad categories that fail to consider deeper lifestyle preferences, resulting in ineffective marketing strategies and decreased sales.

A customer-centric approach to segmentation focuses on individual needs and interests, leading to more personalized service offerings. Modern customer segmentation goes beyond basic socio-economic profiles to include:

  • Pre-purchase and post-purchase behavioral information
  • Online behavior and social media presence
  • Lifestyle details like hobbies and occupational risks
  • Personal health information, including pre-existing conditions and family health history

This detailed profiling allows for more accurate and effective customer targeting

Insurance companies, particularly those using platforms like Microsoft Dynamics 365 or the Power Platform, are turning to business rules engines to manage complex customer segmentation processes. These engines enable insurance analysts to manage and update thousands of rules, adapting to changing market conditions without extensive coding.

Key areas where business rules engines aid in customer segmentation include:

  • Underwriting and risk assessment
  • Determining product eligibility
  • Rating and pricing
  • Policy quoting and renewal.

BREs in Premium Calculation

BREs for more personalized insurance products

The insurance industry, known for its dynamic nature, heavily relies on accurate and efficient premium calculation. This crucial aspect of insurance underwriting involves assessing various risk factors associated with a policyholder to determine the appropriate premium amount. Historically, this process has been manual, involving actuaries analyzing factors like age, gender, occupation, health history, and claims history. However, this traditional method is often time-consuming, error-prone, and challenging to scale​.

The implementation of BREs allows for the automation of complex decision-making processes involved in premium calculation. This automation is particularly beneficial in handling the myriad of variables that influence premium rates, such as policyholder demographics, type of coverage, risk factors, and historical data. BREs can process these variables quickly and consistently, ensuring that premium calculations are both accurate and reflective of the actual risk.

Moreover, BREs facilitate a more dynamic approach to premium calculation. In the rapidly evolving insurance market, factors affecting premiums can change frequently. BREs allow insurers to quickly adjust their models in response to new data or regulatory changes, something that would be cumbersome and time-intensive with traditional methods. This agility ensures that insurance products remain competitive and relevant.

Furthermore, the use of BREs in premium calculation enhances the transparency and traceability of the underwriting process. With a BRE, every decision and calculation is recorded, making it easier to audit and review the process for accuracy and compliance with industry regulations. This transparency is crucial not only for internal governance but also for maintaining customer trust.

Additionally, BREs open the door for more personalized insurance products. By analyzing a vast array of data points, BREs can offer more tailored premium rates based on the individual risk profile of each policyholder. This personalization can lead to better customer satisfaction, as clients receive rates that accurately reflect their specific circumstances.

Advantages of Business Rules Engine

One of the main advantages of business rules engines is their simplicity and accessibility, which allows non-technical subject-matter experts to make changes to calculation rules without IT department assistance. This flexibility is crucial as it enables quick updates to rules in response to changing business needs, regulations, and market conditions, maintaining accuracy and speed in the premium calculation process​​​​.

These engines automate processes, reducing manual effort and increasing efficiency. This automation enables insurance companies to handle a higher volume of policies and respond quickly to customer inquiries​​. In other words, the best way to implement business rules is to use them as a part of business process automation tools

Business rules engines facilitate the incorporation of granular customer data and risk factors into premium calculations, enabling the creation of personalized pricing models. These models are tailored to individual policyholders’ lifestyles and needs, ensuring a fair and competitive pricing strategy​​.

With the automation capabilities of business rules engines, insurers can generate real-time quotes based on accurate risk assessments. This responsiveness allows customers to receive instant pricing information, significantly boosting the customer experience​​.

The use of business rules engines in premium calculation has proven indispensable for insurance companies. By automating decision-making based on predefined rules, insurers achieve higher accuracy, streamline operations, and deliver a superior customer experience. Furthermore, these engines enable insurance companies to explore new business opportunities, stay competitive, and fulfill their commitment to protecting policyholders while generating revenue​​.

Get a personalized evaluation of Higson's potential for your use case
More stories

Streamlining Payroll Management: Innovative Approaches and Technology Integration

Revolutionize payroll with innovative tech! Explore cloud solutions, automation, AI, and rule engines like Higson for accuracy and efficiency.


Optimizing Supply Chain Decisions: Rule Engines for Enhanced Inventory Management

Integrate rule engines, data analytics & IoT for optimized inventory management. Leverage real-time insights for automated decision-making to enhance efficiency.


Enhancing Fraud Detection in Banking with Rule-Based Decision Engines

Enhance your fraud detection with the combination of machine learning, AI, and rules engines. Save your money and protect your clients efficiently.