Fraudulent activity in health claims management remains one of the biggest cost drivers for insurers and healthcare payers worldwide. From duplicate claim submissions to exaggerated treatments or billing for services never rendered, the scale and complexity of fraud make detection increasingly difficult — especially when operating with traditional manual processes or fragmented systems.
One way to significantly enhance fraud detection and prevention is by implementing a Business Rules Engine (BRE). Tools like Higson enable insurers to define, manage, and execute rule-based logic that identifies anomalies and enforces consistent decision-making in real time.
How Business Rules Engines Work in the Claims Process
A Business Rules Engine allows organizations to separate decision logic from application code. This means rules can be maintained and updated independently of the core system, enabling business users to define how certain scenarios should be handled — without involving IT teams.
In the context of health claims management, the BRE serves as an intelligent decision layer. As claims data is submitted — often in large batches — the engine evaluates each claim based on predefined business rules. These rules can cover a wide range of criteria, including:
- Duplicate submission detection: Checking whether a similar claim was already paid within a defined time window.
- Policy and coverage validation: Verifying that the treatment is covered under the patient’s policy and that limits have not been exceeded.
- Provider behavior analysis: Flagging unusual billing patterns or excessive frequency of certain procedures.
- Cross-claim correlation: Identifying inconsistencies across claims submitted by the same policyholder or provider.
By running all submitted claims through this rule framework, insurers can automatically approve, flag for manual review, or reject claims that deviate from expected behavior.
Example Use Case: Preventing Double Payouts
Let’s say a policyholder submits a reimbursement claim for a specialist consultation. A few days later, a second claim arrives — this time submitted by the medical provider — for the same consultation. Without automation, these could both be processed and paid independently.
With Higson's rules engine, a rule can be created to detect when two claims contain matching parameters such as:
- Date of service
- Procedure code
- Policyholder ID
- Provider ID
If a match is found and one of the claims has already been approved, the business rules engine systems can automatically:
- Flag the second claim as a potential duplicate
- Block further processing
- Notify the fraud team or trigger additional rule-based investigation steps
This reduces financial leakage and ensures consistency in fraud detection — without relying solely on manual review.
Benefits of Using a BRE for Health Claims Anti-Fraud
1. Real-Time Detection
Higson’s rules are evaluated at runtime, enabling real-time identification of suspicious claims before payment is issued.
2. Business-Led Rule Management
Non-technical users can define, modify, and test rules in an intuitive interface, which is especially important in fast-changing fraud environments.
3. Auditability and Transparency
Each decision is logged and traceable, which is critical for internal audits, compliance reporting, and regulator requests.
4. Scalability
The BRE can process thousands of claims per second, making it suitable for high-volume environments without degradation in performance.
5. Modular Integration
Higson can operate as an independent engine or be integrated into existing claims platforms through APIs, reducing disruption during implementation.
Conclusion
In today’s fast-moving health insurance environment, relying on after-the-fact fraud detection is no longer enough. A Business Rules Engine like Higson allows insurers to proactively evaluate claims, spot irregularities, and enforce consistent rules across all submissions — all in real time.
By enabling smarter, faster, and more transparent decisions, BREs become a key component in building a resilient, fraud-aware claims ecosystem — where legitimate claims are paid promptly, and fraudulent activity is stopped before it costs money.
