Artificial Intelligence (AI) and Business Rules Engines (BREs) are no longer separate technologies. Together, they form a powerful duo that combines predictive insights with transparent, auditable decision logic.

Introduction: A New Era in Decision-Making Is Here

Making decisions is the essence of business. Some are strategic, like introducing a new product line, while others are operational, like authorizing a customer discount. The need for speed, openness, and flexibility is what ties them together.

Applications with decision-making logic were hardcoded for years. Every change necessitated IT intervention, release cycles, and testing, whether it was changing eligibility requirements, updating a tariff, or modifying underwriting rules. This inflexibility turned into an expensive bottleneck in regulated sectors like finance or insurance.

To address this issue, Business Rules Engines (BREs) were developed. BREs allow business users, not just developers, to independently define, test, and implement rules by removing decision logic from application code. In addition to offering transparency and auditability, this reduces the time-to-market from months to hours.

With its predictive capabilities, artificial intelligence (AI) is revolutionizing a number of industries, including fraud detection, customer behavior prediction, and offer customization. However, AI models are frequently viewed as “black boxes” that are challenging to audit, control, or explain.

When AI and BRE come together, the real breakthrough occurs. They work together to create systems that are more intelligent, quicker, and completely compliant by fusing explainable, governed logic with predictive intelligence.

A Business Rules Engine: What Is It?

Outside of the main application code, a BRE oversees and carries out business decision logic. The engine stores, arranges, and runs rules rather than integrating “if/then” statements into software.

These rules can be expressed as functions, decision tables, or, more and more, as visual flow diagrams. Reliance on IT teams can be decreased by allowing business analysts, actuaries, and product owners to work directly in the BRE.

In the words of Higson’s creator, Marcin Nowak:

Weeks may pass before even the most basic code change is made. The same modification can be made in a matter of hours using a rules engine.

Common applications for BRE:

• Calculations of tariffs and premiums
• Configuration and eligibility for the product
• Automation of underwriting
• Validation and enrichment of data
• Routing and process automation

The fundamental principle: business regains authority over decision-making.

Complementary Forces: AI and BRE

Although BRE and AI have different uses, when combined, they are much more potent.

• AI makes predictions.
  It addresses probabilistic questions:
  “What is the likelihood that this claim is fraudulent?”
  “Which offer is this customer going to accept?”
• BRE is in charge.
  It applies deterministic rules:
  “Send for manual review if fraud score > 0.7.”
  “Apply young driver surcharge if the customer is under 25.”

The combination of BRE and AI guarantees that:

• Consistent operationalization of AI insights
• Business rules continue to be open and auditable
• Regulation adherence is upheld
• Decisions are intelligent and comprehensible

The combination of BRE’s governance and AI’s predictive capabilities is what makes them so alluring, particularly in highly regulated sectors like banking and insurance.

How Integration Operates: The ONNX Method

Models must be simple to implement, run, and update in order for a BRE to fully utilize AI.

Higson uses the ONNX (Open Neural Network Exchange) standard to incorporate AI models:

• It is possible to convert AI models trained in frameworks like PyTorch or TensorFlow into ONNX.
• Efficient execution is guaranteed by the ONNX Runtime.
• When making decisions, the AI model receives input data from BRE.
• The BRE then receives the model predictions for a final rule-based assessment.
• It is possible to update models without having to rewrite or relaunch the entire application.

For instance, in the detection of fraud:

1. The BRE receives customer data.
2. Eligibility checks and other basic business rules are implemented.
3. A fraud probability score is produced by the ONNX model.
4. The score is interpreted by the BRE as approve, flag, or reject.

Because of this smooth integration, AI can improve decisions without compromising the structure, control, and audit trail that BREs offer.

In Conclusion

Business rules engines and artificial intelligence together represent a paradigm shift in corporate decision-making.

By revealing hidden patterns and producing insights from enormous datasets, artificial intelligence (AI) offers predictive power. BRE makes certain that those insights are used in a transparent, organized, and legal manner. When combined, they give businesses the ability to respond swiftly, customize choices, and keep complete control over operational and regulatory requirements.

In real life, this translates into:

• more rapid product launches,
• more precise risk assessments,
• more efficient operations,

while granting business teams the independence to handle logic independently of IT.