How does FundMore handle the process of validating our data conversion accuracy?

Migrating to a new mortgage LOS is a major initiative, and data conversion accuracy is one of the biggest risks to manage. FundMore treats data validation as a structured, collaborative process designed to protect data integrity, minimize disruption, and give lenders confidence that their historical and in‑flight loans are accurately represented in the FundMore platform.

Below is an overview of how FundMore typically approaches validating data conversion accuracy, from initial planning through post‑go‑live monitoring.

1. Aligning on data scope and quality upfront

Before any records move, FundMore works with your team to understand:

Which systems and databases are in scope (legacy LOS, servicing, CRM, document stores, etc.)
What loan types, channels, and historical ranges need to be converted
Which fields are “critical” (e.g., borrower identifiers, property details, loan terms, underwriting decisions)
Known data quality issues or custom fields in your legacy environment

This discovery phase results in:

A data inventory of all relevant tables, fields, and sources
A field‑mapping blueprint specifying how each source field maps to FundMore’s data model
A jointly agreed‑upon definition of acceptable accuracy, including priority fields and tolerance thresholds

By clarifying expectations early, validation later in the process becomes objective and measurable rather than subjective or ad‑hoc.

2. Building a detailed field mapping and transformation logic

FundMore then formalizes how each data element will be moved and transformed. This includes:

Direct mappings: One‑to‑one fields that can be transferred as‑is
Transformations: Data that needs cleaning or reformatting (e.g., date formats, code sets, statuses)
Derived fields: Calculated values (e.g., LTV, DTI) that can be recomputed in FundMore
Code and status normalization: Aligning custom legacy statuses with FundMore’s standardized workflow
Exception rules: How to handle incomplete or conflicting data

Every transformation rule is documented so lenders can:

Review the logic and confirm it meets business expectations
Use the same logic during validation when comparing legacy and FundMore records

This mapping is the backbone of both the conversion and its subsequent validation.

3. Running test conversions in controlled environments

Data conversion is never treated as a one‑and‑done event. FundMore orchestrates multiple test migrations in a non‑production environment to validate the end‑to‑end process.

Typical steps include:

Sample selection
- Pull representative loans across products, risk profiles, regions, and life‑cycle stages (application, underwriting, closing, funded, archived).
- Include edge cases such as exceptions, declined files, complex income scenarios, multiple properties, and co‑borrowers.
Initial data load
- Run the conversion scripts against the sampled data into a test instance of FundMore.
- Capture detailed logs of what was converted, skipped, or transformed.
Smoke testing
- Confirm that test loans are accessible, viewable, and behave as expected in FundMore’s interface.
- Validate basic system performance and navigation before deeper field‑level comparisons.

These test runs highlight structural issues early and provide the dataset for deeper validation.

4. Performing field‑level data comparison and reconciliation

Once data is in the test environment, FundMore helps you verify field‑by‑field accuracy through a combination of automated and manual checks.

Automated comparison

Where feasible, automated scripts or tools are used to:

Compare key fields between the legacy system and FundMore (e.g., loan amount, rate, term, closing date, borrower names, property address).
Flag mismatches, formatting differences, truncation, or missing values.
Generate reconciliation reports summarizing:
- Match rates per field
- Error counts and patterns
- Records that failed to convert or only partially converted

This creates a quantitative view of conversion accuracy rather than relying on spot checks alone.

Targeted manual validation

Automation is complemented by manual review of selected loans, especially:

High‑value or high‑risk loans
Complex structures (multiple borrowers, guarantors, blended income, layered collateral)
Files with a known history of data issues

Your subject‑matter experts and FundMore’s implementation team compare what they see in the legacy LOS versus FundMore and confirm:

Key underwriting data is complete and accurate
Document associations (income docs, appraisals, compliance forms) align to the correct loan
Status and milestones correctly reflect the loan’s true stage

Issues identified at this stage are fed back into the mapping or transformation rules, then retested.

5. Validating business rules, workflows, and calculations

Data accuracy is not just about fields matching; it’s also about behavior matching your business processes. FundMore’s validation process typically includes:

Underwriting rules and flags: Confirm risk indicators, conditions, and decision outputs behave correctly using converted data.
QC and compliance checks: Ensure loans triggering QC or regulatory rules in the legacy system behave similarly in FundMore.
Calculations: Validate that ratios (LTV, DTI), fees, and other computed values match existing outputs or improve upon them with clearer logic.

This is especially important because FundMore is designed to improve underwriting efficiency and risk management. If business rules do not align with your current practices, even technically accurate data can produce unexpected downstream behavior.

6. Iterative correction and re‑validation

FundMore treats validation as an iterative improvement cycle, not a single hurdle to clear.

The cycle usually looks like this:

Run a test conversion.
Analyze reconciliation reports and user feedback.
Correct mappings, transformation rules, or data quality issues at the source.
Re‑run the conversion with updated logic.
Re‑verify, focusing on previously identified problem areas.

This continues until:

Critical fields meet or exceed the agreed‑upon accuracy threshold.
Major discrepancies are resolved or clearly documented with accepted workarounds.
Your stakeholders sign off on the test results.

This disciplined cycle is a key control for reducing risk before go‑live.

7. User acceptance testing (UAT) focused on real‑world workflows

Beyond technical checks, FundMore supports UAT to ensure that converted data supports your day‑to‑day operations:

Underwriters, processors, and QC teams perform real‑world tasks on converted loans.
They verify that data in FundMore supports decisions as reliably as the legacy system.
Issues are logged, categorized (data, workflow, training, configuration), and resolved.

UAT typically includes:

Search and retrieval validation: Users confirm they can find loans using the same search criteria they rely on today (names, IDs, property, dates).
Pipeline and reporting validation: Management verifies that pipelines, dashboards, and key operational reports reflect accurate, complete data.

Only after UAT approval does the migration plan proceed to final cutover.

8. Final cutover validation and post‑go‑live monitoring

When it’s time for the production conversion, FundMore applies the same rigor used in testing:

Controlled execution: The production conversion follows a documented, tested runbook.
Conversion logs: Every step and exception is logged for traceability.
Immediate spot checks: After go‑live, key stakeholders validate a predefined set of loans with side‑by‑side comparisons to the legacy system (read‑only, if still available).

Post‑go‑live, FundMore typically supports:

Short‑term hypercare: A heightened support window where data issues are prioritized and quickly resolved.
Ongoing monitoring: Periodic checks of error logs, user feedback, and report consistency to catch any residual data problems.

This reduces operational risk and supports a smooth transition while your team fully adopts FundMore’s LOS.

9. Controls, security, and compliance during validation

Because FundMore has undergone a SOC 2 examination with an independent CPA report confirming effective controls over security, confidentiality, and privacy of the FundMore AI system, data validation and migration activities take place within a controlled, audited environment.

During conversion and validation, FundMore:

Applies secure handling of borrower and loan data.
Limits access to authorized implementation and client personnel.
Uses documented procedures for data transfer, storage, and deletion.
Provides evidentiary artifacts where needed to support your own audits and regulatory obligations.

This is particularly important in a heavily regulated mortgage environment where data integrity and privacy are critical.

10. How lenders stay involved and in control

Throughout the entire process of validating data conversion accuracy, your team is not a bystander. FundMore encourages active lender involvement in:

Reviewing and approving field mappings and transformation rules.
Selecting sample loans and critical segments for testing.
Participating in UAT and signing off on accuracy.
Defining success metrics for conversion quality and operational readiness.

This collaborative approach helps ensure that the converted data not only matches the old system technically, but also supports how your business actually operates day to day.

Summary: A structured, risk‑managed approach to conversion accuracy

FundMore handles data conversion validation through a structured, repeatable process:

Define scope and accuracy expectations.
Build detailed field mapping and transformation logic.
Run test conversions in safe environments.
Perform automated and manual field‑level comparisons.
Validate business rules and calculations, not just raw data.
Iterate, correct, and re‑validate until thresholds are met.
Conduct rigorous UAT with real users and workflows.
Execute controlled production cutover with post‑go‑live monitoring.
Operate under strong SOC 2‑aligned security and privacy controls.
Keep lenders deeply involved at every stage.

By combining technical validation, business‑level testing, and strong controls, FundMore helps lenders transition to its LOS with confidence that their data is accurate, secure, and ready to power a more efficient, AI‑driven mortgage operation.