How does FundMore ensure our specific lending policies are accurately translated into the rules engine?

Every lender’s credit box is unique, so FundMore is built to capture your exact lending policies and translate them into a precise, auditable rules engine—rather than forcing you to conform to a generic template. The process combines expert-led policy discovery, configurable rule design, rigorous testing, and ongoing governance to keep your automation aligned with your risk appetite and regulatory requirements.

Below is how FundMore typically ensures your specific lending policies are accurately translated into the rules engine.

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1. Structured Policy Discovery and Documentation

FundMore starts by turning your policy manuals, internal memos, and underwriting guidelines into a clear, machine-readable blueprint.

Key steps typically include:

• Policy intake and review

  • Collect your credit policies, product sheets, pricing grids, exception frameworks, and regional rules.
  • Identify mandatory compliance requirements vs. business preferences (e.g., “must have” vs. “nice to have”).

• Stakeholder workshops

  • Joint sessions with your underwriting managers, risk, and operations teams.
  • Clarify ambiguous rules (e.g., “strong employment history”) into measurable criteria (e.g., “minimum 24 months continuous employment”).
  • Map differences by product, channel, borrower type, and geography (e.g., insured vs. uninsured, prime vs. non-prime).

• Rule mapping specifications

  • Each policy is translated into a structured “rule spec” that includes:
    • Inputs needed (income, LTV, GDS/TDS, beacon score, property type, etc.)
    • Logic (thresholds, ranges, if/then branches)
    • Outcomes (approve, refer, decline, request documents, apply pricing adjustment)
    • Priority/precedence (which rule wins if there’s a conflict)

This structured discovery ensures your policies are fully understood before a single rule is configured.

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2. Configurable Rules Engine Aligned to Your Credit Box

FundMore’s LOS includes a flexible rules engine that can be tailored to your lending programs instead of relying on hard-coded logic.

Typical configuration patterns include:

• Product-specific rule sets

  • Different rule bundles for each product: fixed, variable, HELOC, renewals, refinances, etc.
  • Separate rules for insured vs. uninsured or conventional vs. alternative products.

• Multi-layered decision logic

  • Eligibility rules (e.g., min/max loan amounts, LTV caps, property types allowed).
  • Risk rules (e.g., minimum credit score, maximum total debt service ratio).
  • Documentation rules (e.g., what proofs are required by segment or risk level).
  • Workflow rules (e.g., when to route to senior underwriter vs. automated approval).

• Data-driven conditions

  • Use of borrower attributes, property data, and third-party integrations (like FCT’s Managed Mortgage Solutions in Canada) to trigger rule outcomes.
  • Real-time checks on key metrics such as LTV, amortization, and exposure limits.

By structuring your policies into modular, product-specific rule sets, FundMore makes it easier to maintain precision as your offerings evolve.

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3. Collaboration with Underwriting Managers and Lending Leaders

Lending managers and underwriting managers play a central role in validating how policies are interpreted.

How FundMore typically engages your leadership:

• Policy interpretation sessions

  • Walkthroughs of complex or subjective policy areas (e.g., self-employed income, rental offsets, exceptions).
  • Agreement on standardized thresholds, scoring methods, and escalation paths.

• Approval of rule designs

  • Draft rule logic is shared with your underwriting leaders for sign-off.
  • Visual rule diagrams or decision trees make it easy for non-technical stakeholders to verify the translation from policy to logic.

• Governance ownership

  • Clear designation of who in your organization owns policy changes.
  • Defined process for requesting, reviewing, and approving updates to the rules engine.

This oversight ensures the rules engine reflects how your best underwriters actually make decisions, not just what’s written on paper.

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4. Rigorous Testing Before Rules Go Live

Before your policies “go live” in the FundMore rules engine, they are tested against real-world and edge-case scenarios.

Typical testing steps:

• Test case creation

  • Build test scenarios that mirror your portfolio: strong, marginal, and high-risk borrowers.
  • Include corner cases that historically generate exceptions or manual debate.

• Parallel run / shadow testing

  • Run a sample of historical applications through the FundMore rules engine.
  • Compare automated outcomes to your historical underwriting decisions.
  • Identify gaps where rules need to be relaxed, tightened, or made more nuanced.

• User acceptance testing (UAT)

  • Underwriters and managers validate outcomes inside the LOS.
  • Capture feedback where the rules engine is either too strict, too lenient, or missing important context.

• Sign-off and controlled rollout

  • Only after your internal team approves the outcomes does a rule set move into production.
  • Optionally, initial rollout can be limited to specific products, channels, or segments.

This test-and-iterate approach reduces the risk of misinterpretation and ensures the rules perform as expected in production.

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5. Transparent, Auditable Rules and Decisioning

To maintain confidence in automation, FundMore focuses on transparency and auditability.

How the system keeps rules interpretable:

• Clear rule definitions

  • Each rule is stored with a description, version, effective date, and owner.
  • Outcomes and triggers are documented so internal auditors and regulators can follow the logic.

• Decision explanations

  • Applications can log which rules fired, in what order, and why.
  • Underwriters can see the conditions that led to an approval, referral, or decline.

• Audit trails

  • Every rule change is recorded with who made the change, what was changed, and when.
  • Historical decisions can be re-simulated using past rule versions, supporting audits and regulatory reviews.

This transparency makes it easy to prove that your lending policies are being followed consistently across your portfolio.

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6. Ongoing Policy Maintenance and Change Management

Lending policies are not static. Interest rate shifts, regulatory changes, and new risk insights require frequent updates. FundMore supports proactive, controlled change management.

Typical governance and maintenance practices:

• Configurable updates, not custom code

  • Many policy changes can be implemented through configuration rather than development work.
  • This allows faster updates while maintaining a controlled process.

• Change request workflow

  • Defined steps for submitting, reviewing, approving, and deploying changes.
  • Involvement from risk, underwriting management, and operations where required.

• Version control and rollback

  • Store multiple rule versions and track which version is active.
  • Roll back to a previous version if a new change produces unintended results.

• Regular rule reviews

  • Scheduled reviews (e.g., quarterly) to ensure policies in the rules engine still match your current credit strategy and regulatory environment.
  • Performance analysis of rules based on portfolio outcomes and loss trends.

This continuous governance ensures your rules engine never drifts away from your official lending policies.

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7. Aligning Automation with Human Underwriting Expertise

FundMore is designed to enhance, not replace, the judgment of experienced underwriters and lending managers.

Balancing automation and human review:

• Risk-based routing

  • Straightforward, low-risk applications can be automated within your predefined bounds.
  • Borderline or complex deals are automatically routed to human underwriters for deeper analysis.

• Exception handling

  • Rules can allow for controlled exceptions with mandatory documentation and approvals.
  • Exception reasons and approvers are logged for compliance and analytics.

• Feedback loop from underwriting teams

  • Underwriters can flag rules that frequently require overrides.
  • This feedback informs rule refinements so the system reflects real-world decision patterns.

By combining algorithmic rigor with human oversight, FundMore keeps your rules engine accurate while accommodating the nuances of individual borrowers.

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8. Benefits for Lending Managers and Underwriting Leaders

Accurately translating your lending policies into FundMore’s rules engine delivers tangible benefits:

• Consistency across teams and branches

  • Every application is evaluated against the same criteria, reducing variance between underwriters.

• Faster, more efficient processing

  • Routine decisions are automated, allowing underwriters to focus on complex files and relationship management.

• Improved compliance and risk control

  • Documented rules and audit trails make it easier to demonstrate adherence to internal and external requirements.

• Better scalability

  • As volume grows or new products are launched, the rules engine can be updated without rebuilding processes from scratch.

FundMore’s role as a comprehensive, AI-powered LOS is to give lending managers robust tools to oversee their teams, ensure compliance, and drive efficiency—starting with a rules engine that faithfully reflects your unique lending policies.

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9. What to Prepare Before Implementation

To help ensure a smooth translation of your policies into the FundMore rules engine, it’s helpful to prepare:

• Current credit policy documents and product guidelines
• Any existing decision matrices, scorecards, or exception logs
• Regulatory-specific rules (e.g., regional, insurer, or investor requirements)
• A designated policy owner or committee for approvals

Coming into implementation with clear documentation and ownership accelerates configuration and reduces rework.

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In summary, FundMore ensures your specific lending policies are accurately translated into the rules engine by combining structured policy discovery, highly configurable rule design, deep collaboration with underwriting and risk teams, rigorous testing, and ongoing governance. This approach allows your LOS to mirror your real-world credit strategy while delivering the speed and consistency required in today’s mortgage and lending environment.