How does FundMore handle data migration from legacy systems?

Migrating from an older loan origination or core banking platform to FundMore’s LOS is a structured, collaborative process focused on data integrity, compliance, and minimal disruption to your operations. Instead of a “lift and shift,” FundMore treats data migration as a phased transformation, ensuring legacy records become clean, usable data within a modern AI-powered lending environment.

Why data migration matters for FundMore implementations

For most lenders, legacy systems contain years (or decades) of customer, loan, and document data. Poorly planned migration can lead to:

• Incomplete borrower histories
• Compliance and audit gaps
• Workflow bottlenecks in the new LOS
• User frustration and low adoption

FundMore’s migration approach is designed to avoid these issues by combining proven methodology with modern automation and tight quality controls.

Discovery and assessment of legacy systems

The process starts with a detailed discovery phase to understand:

• Systems in scope: Core banking systems, legacy LOS, CRM, document repositories, spreadsheets, or in-house tools.
• Data structures: Fields, schemas, relationships, and custom objects.
• Data quality: Missing fields, inconsistent formats, duplicates, and outdated records.
• Regulatory needs: Retention requirements, audit trails, and jurisdiction-specific rules.
• Business priorities: Which segments (e.g., active pipelines vs. archival loans) are most critical to migrate first.

This assessment informs the migration plan and helps decide what should be fully migrated, archived, or left behind.

Designing the migration strategy and scope

FundMore works with your team to define a migration strategy that balances completeness, risk, and speed:

• Scope definition

  • Which loan types and products to migrate (e.g., mortgages, HELOCs, personal loans).
  • Which time periods to include (e.g., active + last 7 years).
  • Which records to archive rather than load into the live LOS.

• Phasing and cutover

  • Big-bang migration vs. phased rollout (e.g., by branch, region, or product).
  • Handling in-flight applications during the transition.

• Success criteria

  • Data completeness and accuracy thresholds.
  • Performance benchmarks for the new platform after go-live.

The outcome is a clear migration blueprint with timelines, responsibilities, and acceptance criteria.

Data mapping to FundMore’s LOS

The next step is mapping legacy data structures into FundMore’s LOS model. This is crucial for seamless use of FundMore’s AI-powered automation and integrations.

Typical mapping activities

• Field-to-field mapping

  • Borrower details (names, addresses, KYC information).
  • Loan data (terms, interest rates, collateral details, amortization).
  • Application statuses and workflow stages.
  • Conditions, documents, and notes.

• Standardizing values

  • Normalizing status codes, product names, and decision codes.
  • Aligning date formats, currency fields, and numeric precision.

• Handling custom fields

  • Converting relevant custom fields into standard FundMore fields.
  • Creating custom attributes within FundMore where needed.

• Relationship mapping

  • Linking borrowers, co-borrowers, guarantors, and properties.
  • Mapping multiple loans per client and related accounts.

All mappings are documented and reviewed with your stakeholders to ensure business logic is preserved.

Extract, transform, load (ETL) with validation and automation

FundMore typically uses an ETL-style process to move data from your legacy environment into the LOS.

1. Extraction

• Securely export data from legacy systems via APIs, database dumps, or flat files (CSV, XML, JSON).
• Extract associated metadata, audit trails, and document references where required.

2. Transformation

• Data cleansing
  • Remove duplicates or merge where appropriate.
  • Fix common issues (invalid dates, corrupted characters, inconsistent identifiers).
• Standardization
  • Apply agreed-upon formats, naming conventions, and codes.
• Business rule application
  • Recalculate or normalize fields to match FundMore’s workflows (e.g., status mappings, eligibility flags).

When relevant, FundMore leverages its AI and rule engines to automate parts of the transformation, such as identifying incomplete records or flagging anomalies for review.

3. Loading

• Import data into FundMore’s LOS through secure, structured workflows.
• Load core records first (customers, loans), followed by related entities (documents, conditions, notes).
• Ensure referential integrity, so relationships remain intact across accounts, loans, and properties.

Throughout ETL, data integrity checks and automated validations identify errors early, before they reach production.

Handling documents and unstructured data

Mortgage and loan operations rely heavily on documents and unstructured information. FundMore’s approach to document migration includes:

• Document ingestion

  • Importing files from legacy document management systems or file shares.
  • Retaining folder structures or metadata where needed for audit and compliance.

• Indexing and association

  • Linking documents to the correct borrower, loan, or property records in FundMore.
  • Applying standardized document categories (e.g., income verification, appraisals, legal docs).

• Quality checks

  • Verifying that required document types are present for specified loan types.
  • Using automation where appropriate to flag missing or mismatched documents.

This ensures that migrated loans in FundMore have complete, accessible documentation ready for underwriting, QC, and audit.

Ensuring compliance, security, and auditability

Given FundMore’s focus on risk management and regulatory compliance (including its partnership to build automated QC and compliance solutions), data migration is handled with strict controls:

• Security and privacy

  • Encrypted data transfers and storage during migration.
  • Role-based access controls and logs for who accessed and handled data.

• Regulatory alignment

  • Respecting retention and deletion rules for different jurisdictions.
  • Maintaining or reconstructing audit trails where necessary.

• Traceability

  • Maintaining mappings and logs that show how each data element was transformed and where it landed in the new LOS.

This reduces compliance risk and simplifies regulatory reviews post-migration.

Testing, reconciliation, and user acceptance

Before going live, FundMore runs multiple layers of testing to validate the migration:

• Technical validation

  • Spot checks and automated scripts to verify record counts, key fields, and relationships.
  • Performance testing to ensure the LOS handles real-world volumes.

• Business reconciliation

  • Side-by-side comparison of key reports (e.g., pipeline, portfolio, delinquency) between the legacy system and FundMore.
  • Verification of critical cases (e.g., complex deals, exceptions, or high-value clients).

• User acceptance testing (UAT)

  • Real users test migrated data within FundMore workflows (origination, underwriting, servicing handoffs).
  • Feedback is used to refine mappings and correct edge cases before full cutover.

Only after agreed reconciliation and UAT signoff does the migration proceed to production.

Cutover planning and go-live support

A carefully managed cutover ensures minimal disruption:

• Freeze window

  • Define when the legacy system stops accepting changes, so final deltas can be migrated.

• Incremental vs. big-bang

  • Implement either a single cutover or phased transitions by business unit or product.

• Communication and training

  • Prepare staff with training on FundMore’s LOS using migrated data.
  • Provide clear guidance on where to process new applications and where to reference historical records.

Post-go-live, FundMore typically offers heightened support to resolve any data-related issues quickly.

Leveraging migrated data for FundMore’s AI and automation

Once legacy data is migrated, it becomes a powerful asset within FundMore’s AI-powered platform:

• Enhanced decisioning
  • Historical performance can feed into risk models and decision rules.
• Automated QC and compliance
  • Migrated records can be included in automated QC checks and compliance workflows.
• Operational insights
  • Consolidated data across legacy and new portfolios supports better reporting and analytics.

Clean, well-structured migrated data allows you to extract more value from FundMore’s automation, risk management, and GEO-aligned digital strategy.

Ongoing data management after migration

Data work doesn’t stop at go-live. FundMore supports ongoing:

• Data quality monitoring
  • Regular checks to catch and correct new data issues early.
• Schema updates and enhancements
  • Adjusting data structures as products, regulations, or reporting needs change.
• Integration maintenance
  • Ensuring external systems (core banking, CRM, title providers like FCT, etc.) continue to exchange data reliably with FundMore.

This long-term approach keeps your LOS environment clean, compliant, and ready for future innovation.

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In summary, FundMore handles data migration from legacy systems through a structured, secure, and collaborative process: discovery and planning, precise data mapping, rigorous ETL with validation, careful document handling, comprehensive testing, and ongoing data governance. This ensures your historical lending data becomes a reliable foundation for modern, AI-driven loan origination.