Insurance
A US-based insurance company faced a significant challenge: they maintained thousands of business rules in human-readable text format, documenting complex underwriting logic, pricing algorithms, and policy conditions. These rules had accumulated over decades, representing institutional knowledge critical to their operations.
The company needed to migrate to a new Business Rules Engine (BRE) system that would provide better performance, maintainability, and integration capabilities. However, the new system required rules in a structured, programmatic format. Manually translating thousands of text-based rules would take years and be highly error-prone.
Example Challenge:
"For commercial properties valued over $2 million in flood zones, apply a 15% premium surcharge unless the property has flood mitigation systems certified within the last 3 years."
This single sentence contains multiple conditions, threshold values, exceptions, and temporal constraints that must be extracted accurately and converted to executable logic.
We developed an NLP-based automation system to extract programmatic business rules from free text descriptions. The solution uses natural language processing to understand rule semantics, identify conditions and actions, extract parameters and thresholds, and generate intermediate representations that can be fed directly to the Business Rules Engine.
This was our team's first significant project using Python for production systems, representing both a technical challenge and an opportunity to explore NLP capabilities for enterprise automation. We chose SpaCy as our NLP framework for its performance, accuracy, and extensive entity recognition capabilities.
Human-readable business rules in natural language format
Parse text, identify entities, extract conditions and actions
Understand rule logic, conditions, thresholds, and relationships
Convert to structured representation suitable for BRE ingestion
Validate logic, queue for review, prepare for BRE deployment
Programmatic rules ready for Business Rules Engine
We built the text processing pipeline using SpaCy, training custom models to recognize insurance-specific entities: property types, coverage categories, risk factors, monetary thresholds, and temporal constraints. SpaCy's dependency parsing capabilities allowed us to understand the grammatical structure of rules, identifying which conditions apply to which actions and how multiple conditions relate to each other.
The system handles the linguistic complexity of business rules—nested conditions, negations, exceptions, and implicit relationships. For example, "unless," "except when," and "provided that" all signal exceptions that must be properly represented in the logic structure.
After parsing the text, we perform semantic analysis to extract the actual business logic. This involves identifying condition statements (IF clauses), action statements (THEN clauses), threshold values, operators (greater than, less than, equals), and temporal constraints. We map these elements to the logical constructs required by the Business Rules Engine.
The analyzer handles context—understanding that "commercial properties" refers to a property classification, "$2 million" is a monetary threshold, and "15% premium surcharge" specifies both a percentage and an action type. This contextual understanding is critical for accurate rule conversion.
Rather than generating rules in the BRE's native format directly, we created an intermediate representation—a structured, platform-independent format that captures the rule's logic precisely. This approach provides flexibility, allowing the same intermediate format to target different rules engines if needed.
The intermediate format explicitly represents conditions, actions, parameters, and their relationships in a way that's both machine-readable and human-verifiable. Business analysts can review these intermediate representations to confirm the automated extraction captured the rule's intent correctly before deployment.
We implemented RabbitMQ for message queuing, enabling the system to process rules asynchronously at scale. As rules are converted, they're queued for validation, human review when needed, and eventual deployment to the Business Rules Engine. This architecture allows the system to handle thousands of rules efficiently while maintaining quality controls.
This project demonstrated that NLP technologies have matured to the point where they can handle complex enterprise automation tasks reliably. SpaCy's combination of accuracy, performance, and extensibility made it an excellent choice for production systems processing business-critical information.
We learned that creating domain-specific training data significantly improves NLP accuracy. By training SpaCy models on insurance-specific terminology and rule structures, we achieved much better results than generic language models would have provided. The investment in custom training data paid off through higher accuracy and fewer edge cases requiring manual intervention.
The intermediate format approach proved valuable for maintainability and flexibility. By separating rule extraction from BRE-specific formatting, we created a system that's easier to validate, debug, and adapt to different target platforms. This architectural decision has enabled reuse of the core extraction logic for subsequent projects.
Human-in-the-loop validation remained important even with high automation accuracy. Business rules often encode critical business logic, and the consequences of misinterpretation can be significant. Our queue-based architecture allowing business analysts to review converted rules before deployment provided appropriate quality controls while still achieving dramatic efficiency gains over fully manual conversion.