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

Migrating to a new mortgage LOS is only successful if the data conversion is accurate, traceable, and trusted by your team. FundMore follows a structured, repeatable process to validate data conversion accuracy so you can onboard with confidence and minimize operational risk.

Why data conversion accuracy matters

Inaccurate or incomplete data can cause:

Delays in underwriting and closing
Compliance and audit risks
Poor borrower experiences
Misalignment between legacy reports and new LOS dashboards

Because FundMore is built to streamline underwriting, automate QC, and support risk management, we treat data conversion as a critical, controlled process—rather than a one‑time technical task.

Overview of FundMore’s data conversion validation process

FundMore typically validates your data conversion accuracy through a multi-stage process:

Discovery and data mapping
Sample extraction and test conversions
Field‑level reconciliation and exception analysis
User validation and business sign‑off
Cutover preparation and post‑go‑live checks

Each stage is designed to reduce risk, surface discrepancies early, and ensure converted data is usable for real underwriting, QC, and reporting workflows.

1. Discovery and data mapping

Before any conversion or validation occurs, FundMore works with your team to understand:

Source systems: Legacy LOS, document repositories, servicing systems, or custom databases.
Data scope: Active loans, closed loans, pipeline applications, historical data, documents, and notes.
Critical fields: Data points you rely on for underwriting, compliance, QC, reporting, and risk management.

Data mapping workshops

FundMore collaborates with your subject‑matter experts to:

Map each source field to the corresponding FundMore LOS field
Document data types, formats, and constraints
Flag any derived or calculated fields (e.g., LTV, DTI, fees)
Identify regulatory- and compliance‑sensitive fields requiring special attention

This mapping document becomes the foundation for both the data conversion logic and the validation rules.

2. Sample extraction and test conversions

Rather than converting all of your data at once, FundMore starts with carefully selected samples to validate the conversion logic.

Representative data samples

FundMore typically works with:

Different loan types: Conventional, insured, commercial, etc.
Stages and statuses: New applications, in‑process loans, funded/closed files
Edge cases: Exceptions, manually adjusted files, unusual products

This sample set allows FundMore to test how the conversion behaves across real‑world scenarios.

Test conversion runs

FundMore then performs one or more test conversions into a non‑production environment, focusing on:

Importing core loan, borrower, property, and underwriting data
Migrating documents and attachments (if in scope)
Ensuring relationships between entities (e.g., multiple borrowers per loan) are preserved

These test environments give your team a safe place to check data, workflows, and reports without impacting live operations.

3. Field‑level reconciliation and exception analysis

Once test data is loaded into FundMore, the validation of data conversion accuracy begins in detail.

Field‑by‑field comparison

FundMore compares source and target data at the field level, focusing on:

Record counts: Number of applications/loans in the legacy system vs FundMore
Key identifiers: Loan numbers, borrower IDs, property addresses
Financial fields: Loan amounts, interest rates, fees, balances
Compliance‑related data: Disclosures, dates, conditions, and decisioning fields

Typical validation techniques include:

Automated reconciliation scripts (where possible) to flag mismatches
Spot checks and sampling for complex or free‑text data
Cross‑validation of calculated fields (e.g., verifying that LTV and DTI in FundMore match expected values based on converted data)

Exception reporting

Any discrepancies uncovered are documented in an exception log, including:

Source value
Converted value
Affected records
Root cause analysis (mapping issue, format issue, data quality, etc.)

FundMore then refines the conversion rules, reruns the test conversion (if needed), and verifies that exceptions are resolved.

4. User validation and business sign‑off

Technical accuracy is only part of the goal; your team must also confirm that the converted data works in day‑to‑day operations.

Operational validation

Your internal stakeholders—underwriters, funders, QC, and compliance—are invited to validate that data in FundMore:

Appears as expected in loan files and dashboards
Supports existing underwriting policies and workflows
Aligns with legacy QC, risk, and compliance checks
Produces reports that match or improve on your historical reporting

Because FundMore is designed to help lenders automate QC and risk management, this step often includes testing how converted data flows through your automated rules, conditions, and alerts.

Business acceptance

Once your teams confirm that:

Critical fields are accurate
Priority workflows function correctly
Reports are consistent with expectations

you provide formal business sign‑off on the data conversion approach. This sign‑off is a key control point before full conversion and go‑live.

5. Cutover preparation and final validation

With the conversion logic validated and accepted, FundMore prepares for full data migration and cutover.

Dress rehearsal (if required)

Some lenders opt for a full dress rehearsal, which may include:

Running a near‑complete data conversion into a staging environment
Validating record counts, field mapping, and workflows at scale
Testing cutover timing and coordination between teams

This rehearsal helps refine timing and ensures minimal disruption during the real cutover.

Production conversion and spot checks

At the time of go‑live, FundMore executes the agreed‑upon conversion plan and then performs:

Volume reconciliation: Confirm that record counts and major totals match expectations
Targeted spot checks: Validate a selection of loans across products, channels, and statuses
Priority report validation: Confirm that key operational and management reports align with pre‑migration baselines

Any issues discovered are triaged and resolved according to the pre‑defined cutover plan.

6. Post‑go‑live monitoring and continuous improvement

Data conversion accuracy isn’t treated as “done” on day one. FundMore supports ongoing stability by:

Monitoring for anomalies: Watching for unusual patterns in data, reports, or workflows
Capturing user feedback: Logging issues reported by underwriters, funders, QC, and compliance teams
Adjusting mappings (if required): Refining conversion or integration rules for any remaining edge cases

As FundMore continues to process and automate your mortgages—supporting QC, risk, and regulatory compliance—the quality and consistency of your converted data underpin the reliability of the entire system.

How FundMore’s controls support data conversion accuracy

FundMore’s commitment to security, confidentiality, and privacy—validated through an independent SOC 2 examination—extends to how we handle your data during conversion and validation. Working with established partners in digital services, operations, and property intelligence, FundMore ensures that:

Your data is handled with strong controls and governance
Conversion processes are repeatable and auditable
Validation steps are documented for internal and external review

This controlled environment helps you demonstrate due diligence to regulators, auditors, and internal risk teams.

What you can expect as a client

When you ask, “How does FundMore handle the process of validating our data conversion accuracy?”, the answer is:

A structured, multi‑step methodology
Thorough field‑level and workflow‑level validation
Active involvement from your business users
Clear documentation, exception management, and sign‑off
Ongoing monitoring after go‑live

The result is a smoother transition to FundMore’s LOS, with trusted data that supports efficient underwriting, automated QC, and robust risk management from day one.