From Decision Support System to Decision Automation: How Insurers Make Smarter, Faster Choices with Higson

Introduction – The Evolution of Decision Support

The concept of a Decision Support System (DSS) has shaped business technology for more than half a century. A DSS is a computer program and informational application that analyzes data to produce comprehensive information reports, helping organizations make more informed and efficient decisions. Originally developed to help executives analyze data and explore “what-if” scenarios, DSS platforms represented one of the earliest forms of business intelligence. They were built to support human judgment through dashboards, reports, and historical insights, and were specifically designed to provide comprehensive information to support informed decisions.

But in the age of real-time data and digital ecosystems, “support” is no longer enough. Insurers now operate in markets that demand instant, compliant, and personalized responses. From underwriting and pricing to claims automation, decision-making happens thousands of times per second.

In this environment, the role of DSS has evolved. It has moved beyond simply presenting information toward actively executing decisions by applying predefined business rules. The modern Decision Support System is not just a database with reports. It is an intelligent automation layer that connects data, rules, and predictive models into one decision fabric.

What Defines Modern Decision Support Systems

A contemporary Decision Support System differs fundamentally from its predecessors. While classic DSS platforms focused on providing analytical insight, today’s systems must also leverage advanced capabilities, such as integration with AI, real-time analytics, and sophisticated automation features. These advanced capabilities enable modern DSS to emulate human decision-making with enhanced performance and versatility.

• Operate in real time, reacting to data streams from multiple sources.
• Enforce business logic consistently across channels and applications through seamless integration capabilities.
• Be transparent and auditable, ensuring every automated decision can be explained.
• Enable business users and non-technical users—not just IT—to configure and adjust decision rules, managing rules efficiently.
• Integrate with AI models and external data, enhancing predictive accuracy.

Many of these features have evolved from early data driven DSS, such as the pioneering system developed by American Airlines in the 1970s, which laid the foundation for today’s data-centric decision support.

This evolution has given rise to what many organizations now describe as Decision Automation Platforms—software systems that don’t just assist decisions but make them automatically, guided by human-defined logic and AI-based insights. Intuitive DSS interfaces, including user-friendly windows, menus, and dashboards, are now essential for easy navigation and effective rule management by business users.

In the insurance industry, where every policy, claim, or rate adjustment involves a web of interdependent variables, such systems are becoming indispensable.

Business Rules and Logic

Business rules and logic are the backbone of any effective decision support system. They serve as the explicit criteria and constraints that guide business decisions, ensuring that every action taken is consistent, compliant, and aligned with organizational goals. Business rules can be thought of as clear statements that define how operations should be conducted, what conditions must be met, and what actions should be triggered in specific scenarios.

By applying predefined business rules, organizations can automate decision making processes, reducing the reliance on manual intervention and minimizing the risk of human error. This automation not only streamlines complex decision making processes but also enables faster, more reliable business decisions across the enterprise. In industries like insurance, where regulatory requirements and market dynamics change rapidly, having robust business rules and logic in place allows companies to adapt quickly and maintain a competitive edge.

A well-designed support system leverages these business rules to ensure that every decision is informed by the latest data, regulatory standards, and strategic objectives. Ultimately, the integration of business rules and logic within a decision support framework empowers organizations to make smarter, more consistent decisions at scale.

Business Rules Engines and Execution

A business rules engine is a specialized software system designed to execute business rules in real time, providing a dynamic layer of intelligence within enterprise applications. Unlike traditional approaches where business logic is hard-coded into application code, a business rules engine allows organizations to define, test, and manage all the rules independently, making it easier to adapt to changing business needs.

Most business rules engines in use today employ forward chaining, meaning they process and execute rules as soon as relevant data is available or when triggered by a user or application. This approach ensures that decision making processes are both responsive and efficient. The rules engine is often a core component of a broader business rule management system (BRMS), which provides tools for rule management, classification, and version control.

By leveraging a powerful tool like a business rules engine, organizations can automate decision making processes, ensure compliance with internal policies and external regulations, and significantly reduce operational overhead. Rule management becomes more transparent and collaborative, enabling business and IT teams to work together to define and refine decision logic without the need for extensive changes to application code. This separation of concerns not only accelerates the pace of innovation but also enhances the reliability and auditability of business decisions.

Why Insurers Depend on Decision Intelligence

Insurance, by nature, is a decision business. Every stage of the insurance lifecycle—product design, risk assessment, pricing, underwriting, claims, and customer management—is defined by structured, rule-based decisions made by decision makers who rely on Decision Support Systems (DSS) to analyze data and generate insights.

For years, these decisions were embedded directly in application code. Adjusting a pricing formula, modifying an underwriting rule, or launching a new product required developer time, lengthy release cycles, and regression testing. As a result, even small regulatory or market changes could take weeks or months to implement.

This lag created a critical gap between data insight and operational execution. Insurers could see what needed to change but lacked the tools to act fast. Modern DSS support decision makers by providing timely insights and recommendations, enabling them to act quickly and effectively.

Modern Decision Support Systems bridge this gap by providing a unified layer where decision logic can be authored, tested, and deployed independently from the core policy administration system. They transform decision management from a coding exercise into a business function.

In practice, this means:

• Product managers can introduce new coverage variants directly.
• Risk teams can adjust eligibility or scoring rules in hours.
• Operations management teams can validate and monitor business logic continuously.

The result is agility, transparency, and resilience - three capabilities that define successful insurers in today’s volatile environment by enabling informed decisions.

From Decision Support to Decision Automation

The transition from traditional DSS to Decision Automation marks a paradigm shift similar to the evolution from static websites to interactive applications.

In the past, a DSS served as a mirror—it reflected the organization’s data and helped experts interpret it. Decision Automation, however, acts as the engine that drives actions. Rule engines are central to this transformation, providing the automated decision-making capabilities that power modern systems. It combines analytics, rules, and AI models to determine outcomes in real time, with rule processing and rule execution enabling real time decision making.

For insurers, the implications are profound. Instead of relying on manual processes or rigid systems, organizations can now:

• Automate underwriting triage based on configurable business rules.
• Adjust pricing dynamically using live data feeds and projected revenue figures based on historical company data.
• Evaluate claims automatically according to eligibility and policy terms.
• Deliver consistent decisions across multiple channels—agent, online, or API.

Decision Automation does not replace human expertise; it amplifies it. By codifying expert knowledge into reusable logic, it ensures consistency while freeing specialists to focus on innovation and strategic analysis.

This transformation is made possible through the rise of Business Rules Engines (BREs) the modern core of intelligent Decision Support Systems. Rule engines and rule engines facilitate scalable, flexible, and transparent automation, while efficient rule execution and rule processing support complex, real-time decision making across industries.

The Architecture Behind Automated Decisions

At the center of every automated DSS lies a structured layer of business logic: the rules that determine what can be sold, to whom, under what conditions, and at what price.

In a traditional IT landscape, these rules are scattered - some in the policy administration system, others in Excel files or separate databases. Maintaining them becomes a challenge as organizations grow.

A Business Rules Engine consolidates this complexity. It stores decision logic in a centralized, versioned repository, allowing business and technical teams to collaborate within a shared framework. Modern DSS platforms enable users to efficiently manage rules, including adding new rules or modifying existing ones, supporting both technical and non-technical users. Each rule, condition, and dependency is explicitly defined, making it possible to modify rules or test decisions without rewriting code.

In essence, a modern DSS built on a BRE provides:

• Centralized logic management: all the rules in one place.
• Separation of business and application layers, reducing IT dependency.
• Version control for tracking rule changes over time.
• Explainability, ensuring every outcome can be traced and audited.

Organizations may also compare their BRE with other rule engines to ensure they select the solution that best fits their integration, flexibility, and decision-making needs.

This design is particularly valuable in regulated industries like insurance, where compliance, documentation, and traceability are as critical as speed.

How Higson Enables Decision Support in Insurance

Higson, Decerto’s Business Rules Engine, represents this new generation of Decision Support Systems. Designed specifically for industries such as insurance and finance, it provides a decision automation layer that complements existing policy administration systems.

Instead of relying on static code or manual configuration, insurers use Higson to define, execute business rules, and monitor decision logic in real time. Product definitions, tariff structures, underwriting criteria, and eligibility rules are all centralized within the engine.

Higson serves as both the knowledge base and the execution layer of a modern DSS. Business users can design logic through an intuitive interface - Higson Studio, which enables users to modify business logic and manage processes without extensive technical expertise, while developers integrate it seamlessly with existing systems via APIs. Higson can also be integrated with business process management platforms, allowing for end-to-end automation by connecting workflows with business rules for event-driven process automation.

This combination allows insurers to:

• Create and test decision rules without code.
• Launch and adjust insurance products faster.
• Ensure full auditability and version control.
• Maintain consistent decision logic across systems and channels.
• Optimize processes such as inventory management for better decision-making.

By bridging the gap between business intent and technical implementation, Higson transforms how insurers design, validate, and operationalize their decision processes.

Case Study: InterRisk and the IRON Platform

InterRisk, a company within Vienna Insurance Group, recognized the growing need for agility in its sales and product configuration processes. Legacy systems, limited by hard-coded logic and fragmented data sources, slowed down the introduction of new products and made it difficult to respond to regulatory changes. Similar challenges are faced across various industries, where organizations seek to modernize and streamline their operations.

To address this, InterRisk launched the IRON (InterRisk Online Network) platform - a new system designed to modernize sales, renewals, and product management. At the heart of IRON lies Higson, powering the decision support and configuration layer.

With Higson integrated as the core decision engine, InterRisk gained the ability to:

• Define and manage decision logic for product eligibility, coverage combinations, and tariff rules.
• Centralize all configuration elements—product definitions, risk mappings, and data dependencies into a single repository.
• Empower business teams to modify and test rules independently from IT.
• Achieve real-time responsiveness to regulatory and market changes.

The implementation brought measurable business outcomes. InterRisk reduced time to market for new product configurations from weeks to hours, strengthened internal governance, and improved user experience for agents working with the IRON platform, who can now access the system via mobile devices for greater flexibility.

Most importantly, the company redefined its decision-making process. Instead of relying on developers to encode business policies, product owners and analysts could model them directly within the DSS environment, ensuring compliance with regulatory standards throughout the process.

The result was not only faster product delivery but also greater transparency, consistency, and scalability hallmarks of an effective decision support system in modern insurance.

Integrating AI and Predictive Intelligence

As Decision Support Systems evolve, they increasingly integrate artificial intelligence and machine learning. This convergence creates what can be described as AI-augmented decision support—a model where business rules and predictive algorithms work together. Expert systems, which leverage rule-based logic and inference engines, can be embedded within DSS to automate complex decision logic and facilitate advanced knowledge management.

In traditional setups, AI models often existed as external components, producing predictions that had to be manually incorporated into business logic. Today, solutions like Higson integrate these models directly into the decision flow through standards such as ONNX (Open Neural Network Exchange). When using open-source solutions, developers benefit from access to the source code, allowing for inspection, modification, and customization of the engine's functionality.

This allows insurers to:

• Use trained models from frameworks like TensorFlow or PyTorch directly within their decision logic.
• Trigger predictive scoring as part of a broader rule evaluation process.
• Update or replace models without changing the underlying application code.

For example, an insurer could integrate a fraud detection model or a predictive risk score into the underwriting flow. The Decision Support System then combines AI output with traditional rules—such as policy eligibility or credit scoring thresholds—to produce balanced, explainable results. Expert systems utilize rule-based reasoning, forward and backward chaining, and are often integrated into programming environments like Python to solve such complex problems.

The outcome is not just automation, but intelligence at scale—a fusion of data-driven prediction and rule-based governance that defines the future of decision-making.

Open-Source Options for Decision Support

Open-source decision support systems have become increasingly popular as organizations seek flexible, cost-effective solutions for automating decision making processes. These systems offer a wide array of tools, including business rules engines, data analytics platforms, and machine learning libraries, all of which can be tailored to meet specific business requirements.

By adopting open-source decision support systems, organizations gain the ability to customize and extend their decision support capabilities without being locked into a single vendor’s ecosystem. Open-source business rules engines and rules engines, for example, allow companies to automate decision making processes, integrate with various data sources, and adapt quickly to new business challenges. The collaborative nature of open-source development also means that organizations benefit from community support, rapid innovation, and a wealth of shared best practices.

For businesses looking to develop or enhance their decision support systems, open-source options provide the flexibility to build solutions that align closely with their unique processes and objectives, all while keeping costs under control and fostering a culture of continuous improvement.

Proprietary vs Open-Source Decision Support

When selecting a decision support system, organizations must weigh the benefits and trade-offs between proprietary and open-source solutions. Proprietary decision support systems are developed and maintained by a single vendor, offering robust integration, dedicated support, and a comprehensive suite of features. These systems often provide seamless decision support, regular updates, and a high level of reliability, but they can come with significant licensing fees and may limit customization options.

In contrast, open-source decision support systems offer greater flexibility and cost savings. They allow organizations to modify and extend the software to fit their specific needs, leveraging a broad ecosystem of tools and community-driven enhancements. However, open-source solutions may require more technical expertise to implement and maintain, and organizations may need to rely on community support rather than dedicated vendor assistance.

Ultimately, the choice between proprietary and open-source decision support systems depends on an organization’s technical capabilities, budget, and strategic priorities. By carefully evaluating both options, businesses can select a decision support approach that best aligns with their operational requirements and long-term goals.

The Business Value of Modern Decision Support

The modern Decision Support System is more than a technical component, it is a strategic enabler. For insurers, its value manifests across multiple dimensions:

1. Agility
   The ability to modify business logic instantly enables rapid adaptation to new regulations, market conditions, or risk models.
2. Transparency
   Every decision is traceable, versioned, and auditable, ensuring full compliance and accountability.
3. Collaboration
   Business and IT teams work within a shared framework, reducing miscommunication and implementation delays.
4. Efficiency
   Automation reduces manual effort, minimizes errors, and accelerates time to market.
5. Customer Experience
   Faster, more accurate decision-making leads to smoother policy issuance, quicker claims processing, and higher satisfaction.
6. Data-Driven Innovation
   Integration with external datasets and AI models enhances analytical capabilities, enabling continuous optimization of business outcomes.

In combination, these several advantages make modern DSS platforms essential to the digital transformation of insurance companies.

Decision Support System Implementation Best Practices

Successfully implementing a decision support system involves more than just deploying new technology, it requires a strategic approach that aligns with business objectives and operational realities. Best practices begin with clearly defining the goals and expected outcomes of the decision support initiative. Engaging key stakeholders from both business and IT ensures that the system addresses real-world needs and integrates smoothly with existing systems and business processes.

A thorough assessment of current data sources, workflows, and business rules is essential for achieving seamless integration and maximizing the value of the new support system. Leveraging business rules engines and rules engines to automate decision making processes can significantly enhance efficiency and accuracy, while also enabling non technical users to participate in rule definition and management.

Training and ongoing support are critical to user adoption, especially for non technical users who will interact with the system’s interface. Continuous monitoring, evaluation, and refinement of the decision support system ensure that it remains effective as business needs evolve. By following these best practices, organizations can build a robust decision support framework that delivers actionable insights, supports informed decision making, and drives sustained business value.

The Future of Intelligent Decisioning

Looking ahead, Decision Support Systems will continue to evolve from static tools into autonomous decision ecosystems. As insurers increase their reliance on real-time data, the boundary between decision support and execution will blur even further.

Several trends are shaping this future:

• Hybrid decision models, where human experts supervise automated decisions, refining the logic based on outcomes.
• Self-learning systems, where AI suggests optimizations to rule sets based on performance metrics.
• Unified decision intelligence platforms, combining data management, rule authoring, and predictive analytics in one environment.
• Regulatory compliance automation, ensuring that every decision aligns automatically with evolving standards.

In this landscape, the role of solutions like Higson becomes even more critical. They provide the governance, structure, and transparency needed to scale decision automation safely and effectively.

The insurers that master this balance, combining automation with explainability will lead the next generation of digital insurance operations.

Conclusion

Decision Support Systems have come a long way from their analytical origins. In the insurance industry, they are no longer about supporting isolated decisions but about managing continuous, automated decision flows across the enterprise.

By integrating business rules, data, and AI into a unified framework, modern DSS applications enable insurers to operate with unprecedented speed, precision, and confidence.

Higson exemplifies this transformation. As a Business Rules Engine designed for decision automation, it empowers insurers to design, test, and execute their logic autonomously, ensuring that every decision—whether about pricing, underwriting, or claims, reflects the company’s strategy, compliance requirements, and market goals.

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