Automated Underwriting Software
How does FundMore handle the migration of historical loan data from our current system?
8 min read
Migrating historical loan data is one of the most important steps in moving to a new LOS, and FundMore approaches it as a structured, collaborative project rather than a simple “lift and shift.” The goal is to ensure your legacy data is accurate, usable, compliant, and ready to support your lending teams from day one on FundMore’s AI-powered loan origination platform.
Below is an overview of how FundMore typically handles the migration of historical loan data from your current system.
1. Discovery and assessment of your current system
FundMore starts with a detailed discovery phase to understand:
- Which systems hold your loan data (core LOS, servicing systems, spreadsheets, imaging systems)
- What types of data you want to migrate (applications, approvals/declines, conditions, documents, notes, underwriting decisions, pricing, funding details, etc.)
- Historical depth (e.g., last 2–7 years, or a specific time window)
- Data standards and formats (CSV exports, database access, APIs, flat files)
- Regulatory or internal retention rules that may affect what can or should be migrated
During this phase, FundMore works with your technical and business stakeholders to define:
- The scope of data migration (which entities and fields will be moved)
- Desired use cases (reporting, trend analysis, compliance audits, servicing handoff, customer 360° view, etc.)
- Priority data sets (e.g., active pipelines and recently closed loans vs older archive data)
This discovery step ensures the migration is driven by business value and compliance, not just technical feasibility.
2. Data mapping: aligning your fields with FundMore
Once the scope is defined, a detailed data mapping exercise converts your legacy data model into FundMore’s structure.
This typically includes:
- Mapping core entities
- Borrowers, co‑borrowers, guarantors
- Loans, products, and programs
- Properties and collateral
- Brokers and referral sources
- Mapping key data fields
- Application dates, statuses, milestones
- Income, liabilities, assets, and credit data
- Appraisal details and risk assessments
- Conditions, documents, and checklist items
- Funding details and disbursement information
- Identifying required vs optional data
- Mandatory fields for compliance and internal policy
- Recommended fields to unlock FundMore’s analytics and AI capabilities
- Defining transformations
- Normalizing status codes (e.g., “Closed–Funded” vs “Funded”)
- Standardizing product names, channels, and sources
- Unifying date/time formats and currencies
- Resolving duplicates and conflicting values
FundMore’s implementation team collaborates closely with your subject-matter experts so the migration preserves meaning and context, not just raw data.
3. Data extraction from your current LOS and related systems
With mapping defined, FundMore supports a structured extraction process from your current system(s):
- Using standard exports (CSV, Excel, XML, JSON)
- Leveraging APIs or database views where available
- Pulling relevant metadata (user IDs, timestamps, audit fields)
- Extracting document references or file paths for imaging systems
FundMore typically works with your IT and vendor teams to:
- Schedule extraction windows, minimizing disruption to daily operations
- Ensure secure transfer of extracted data to the FundMore environment
- Validate that the exported data matches the agreed scope (record counts, date ranges, statuses)
This step lays the foundation for a clean and secure migration.
4. Data cleansing and normalization
Raw historical data from legacy systems often contains inconsistencies or gaps. FundMore incorporates a cleansing and normalization step to improve data quality before it enters the LOS:
- Standardizing values
- Statuses, product codes, branch/region names, and channel codes
- Handling missing or invalid values
- Filling defaults where allowed
- Flagging incomplete or non-compliant records for review
- De‑duplicating records
- Identifying duplicate borrowers, loans, or properties across systems
- Aligning with your current policies
- Removing deprecated products or codes
- Mapping old fields to new regulatory or business requirements
Cleansing rules are documented and agreed in advance so there’s a clear record of how legacy data was transformed.
5. Secure loading into FundMore’s LOS
After cleansing and mapping, FundMore loads the data into your tenant of the LOS using secure, controlled processes:
- Batch import pipelines designed for high-volume loading
- Validation checks on each batch (record counts, mandatory fields, referential integrity)
- Error handling and logging for any records that fail to import
- Audit trails documenting when, how, and by whom data was loaded
Security is maintained end to end:
- Encrypted data in transit and at rest
- Access controls and permissions in the FundMore platform
- Alignment with your institution’s security and compliance requirements
The result is a migrated data set that is not only present in the system, but also auditable and traceable.
6. Preserving data history and auditability
Historical loan data is often critical for compliance, audits, and internal reporting. FundMore designs migrations to preserve this history wherever possible:
- Retaining original dates (application, approval, funding, maturity, etc.)
- Preserving user IDs and timestamps where provided by your source system
- Capturing historical status changes when your data model supports it
- Maintaining links to documents, notes, and conditions
Where a one‑to‑one translation of audit history isn’t technically possible, FundMore can:
- Store legacy reference IDs and key dates
- Keep snapshots or reference tables for historical reporting
- Provide exportable logs of the migration process for audit documentation
This approach helps you demonstrate continuity across platforms and meet regulatory expectations.
7. Handling documents and attachments
Loan files are more than structured data; they include documents, images, and other attachments. FundMore supports multiple strategies for document migration based on your requirements and current setup:
- Direct migration of documents into FundMore’s document management layer
- Mapping existing document types to FundMore categories
- Retaining file naming conventions or applying new standards
- Hybrid approach
- Migrating recent and active loan documents
- Keeping older documents in an archive with links or reference IDs
- Archive-only approach
- Maintaining legacy imaging systems for older files
- Migrating only metadata and pointers to the legacy storage
Decisions around document migration are typically driven by:
- Regulatory retention requirements
- Access needs for your front-line and back-office teams
- Cost and effort considerations for moving large volumes of files
8. Pilot migration and user validation
Before full-scale migration, FundMore typically conducts a pilot or test migration:
- Selecting representative data sets
- A mix of active pipeline files, recently closed loans, and older historical files
- Running end-to-end migration
- Extraction → mapping → cleansing → loading → validation
- Engaging business users for validation
- Verifying that key fields display correctly in the LOS
- Testing search, reporting, and workflow interactions with migrated data
- Confirming that conditions, documents, and statuses appear as expected
Feedback from this pilot informs refinements to mappings, rules, and processes before the final migration.
9. Full migration and cutover planning
Once the pilot is successful, FundMore works with you to plan and execute the full migration and system cutover:
- Scheduling the final data extraction near go‑live
- Ensuring that the latest updates in your legacy system are captured
- Minimizing downtime
- Aligning cutover with low-volume periods where possible
- Defining clear timelines and responsibilities for each team
- Running reconciliation checks
- Comparing record counts and key metrics between legacy and FundMore
- Confirming that all in-scope loans and borrowers are present and accurate
A coordinated cutover plan helps your teams transition smoothly to FundMore while maintaining operational continuity.
10. Post-migration support and optimization
After go‑live, FundMore continues to support your team as you work with migrated historical data:
- Monitoring data quality and performance
- Addressing any residual mapping issues or missing data sets
- Fine-tuning reports and dashboards that rely on historical data
- Leveraging historical data to power analytics and AI models
Because FundMore is an AI-powered loan origination platform, using your historical data effectively can enhance:
- Risk and fraud detection
- Portfolio analysis and performance tracking
- Process optimization and SLA monitoring
- Borrower experience improvements based on past behavior and outcomes
11. Customization based on your institution’s needs
Every lender’s data landscape is unique. FundMore adapts its migration approach to:
- The size and complexity of your loan book
- The number and type of legacy systems involved
- Your regulatory environment and internal risk appetite
- Your strategic goals (e.g., digitization, AI-driven underwriting, borrower self-serve)
Options may include:
- Migrating all historical loans vs a defined time window
- Prioritizing specific portfolios (e.g., residential vs commercial)
- Staged migrations by branch, region, or business line
- Parallel runs, where FundMore and your legacy system operate side-by-side for a period
By tailoring the migration strategy, FundMore helps ensure both a safer transition and faster time-to-value.
12. What lenders can do to prepare for a smooth migration
To make the migration of historical loan data into FundMore’s LOS as efficient as possible, lenders can:
- Inventory all systems where loan-related data resides
- Identify must-have vs nice-to-have historical elements
- Clean up obvious data quality issues in advance (duplicates, obsolete codes)
- Clarify regulatory retention and audit requirements
- Designate internal data owners and decision-makers for mapping and rules
Working collaboratively with FundMore’s implementation and data teams, you can significantly reduce risk and accelerate adoption of the new LOS.
In summary, FundMore handles the migration of historical loan data through a structured, secure, and business-driven process: discovery, mapping, extraction, cleansing, loading, validation, and ongoing optimization. The approach is designed to preserve the integrity and usefulness of your historical data so that, once on FundMore, your teams can leverage both legacy and new-originations data to improve lending performance, compliance, and borrower experience.