What to Look for When Choosing a Business Rule Engine for LOS

Not every business rule engine on the market is designed to effectively support lending workflows within a Loan Origination System. To ensure your LOS performs at its best, it’s essential to evaluate several critical factors before selecting the right BRE.

Factors to Consider While Choosing the BRE

Criteria	Feature
Domain Specialisation	Workflows tailored for lending
Integration	API-first, cloud-native design
Scalability	10,000+ decisions/sec, low latency
Access Control	Role-based rule and audit management
AI Compatibility	Supports ML integration, hybrid logic

Domain Specialisation

Generic rule engines may offer flexibility, but they often fall short in handling the intricacies of financial services. Lenders face regulatory constraints, credit risk management models, real-time fraud checks, and underwriting workflows that demand domain-specific logic.

Specialised BREs that are designed to best fit LOS come with:

• Pre-configured rules for KYC checks, debt-to-income ratios, and collateral valuation
• Built-in compliance frameworks for the Fair Lending Act and BASEL III requirements
• Industry-specific decision trees for auto loans vs. mortgages vs. SME lending

Integration Capabilities: API-First and Cloud-Native Design

Modern loan origination system environments are interconnected with numerous external and internal systems, ranging from CRM platforms and KYC services to credit bureaus, fraud detection tools, and core banking systems. To enable real-time lending, your BRE must be API-first, ensuring seamless and scalable integration with each of these components. These integrations should not only be robust but also optimised for performance. For example, a latency of 50 to 100 milliseconds is considered ideal for API calls in real-time decisioning, and this applies to the BREs used in LOS as well.

Why Choose a Cloud-Native Business Rule Engine (BRE)?

• Seamless Deployment: Cloud-native BREs can be rapidly deployed across your infrastructure with minimal setup and configuration, reducing time-to-market.
• Anywhere, Anytime Access: Users can securely access the BRE from any location or device, enabling remote work and real-time collaboration.
• Automatic Updates and Maintenance: Cloud providers manage software updates, patches, and backups, freeing your IT team to focus on strategic initiatives.
• Integration Flexibility: Easily connect with other cloud-based services and APIs, enabling a more agile and interconnected Loan Origination System.
• Disaster Recovery and High Availability: Cloud-native BREs typically include built-in redundancy and failover mechanisms, minimising downtime and data loss risks.

Scalability: Can It Handle Thousands of Decisions per Second?

As digital lending scales, your BRE must support high-throughput processing without latency. Whether you’re handling microloans, BNPL, or mortgages, peak performance should not lead to system slowdowns. For example loan platforms might have to handle 3,000+ decisions/second during peak seasons without performance degradation.

For example, Corestrat’s Rule.ai, a customisable business rule engine (BRE) designed for industry-specific purposes, helps lending institutions design rules and strategies to automate decision-making. It also scales with the institution’s growth and efficiently handles loan applications, allowing lenders to focus on achieving their business goals.

User Access Control: Role-Based Access for Rule Editors vs. Auditors

Security and governance in lending are non-negotiable. Rule management should be role-based, ensuring that only authorised users can modify logic while auditors and risk officers retain view-only access.

Role-Based Access Configuration

Role	Permissions
Admin	Create/Edit/Delete rules, Manage users
Rule Editor	Edit business rules
Auditor	Read-only access, View logs
IT Admin	Infrastructure and deployment

For example, an underwriter needs to adjust a rule for self-employed applicants, while a compliance officer must validate that the changes don’t violate lending norms.

With role-based access, the BRE ensures:

• Separation of duties
• Audit trails for every rule change
• Compliance readiness for regulators

Sample Implementation Checklist:

• 4-eye principle: Require dual approval for production rule changes
• Version control: Maintain 180-day change history with rollback
• Field-level encryption: Protect sensitive data like Social Security Numbers and credit scores

Support for AI Models: Hybrid Engines for Rule + ML Synergy

The future of automated decisioning lies in hybrid engines—those that can blend deterministic rules with probabilistic machine learning (ML). For instance:

• Rules: “Reject applications with CIBIL score < 600”
• ML: Predict default risk for borderline applications

By integrating models directly into the decision flow, you gain both explainability and intelligence.