What data does FundMore AI use to make underwriting decisions?

FundMore AI uses a wide range of borrower, property, and loan data to help lenders make faster, more consistent, and more accurate underwriting decisions. Instead of replacing a lender’s credit policy, FundMore’s AI-powered loan origination platform ingests and analyzes the data that matters most for mortgage risk, compliance, and operational efficiency.

Below is a breakdown of the key data types FundMore AI uses in the underwriting process, and how they feed into automated assessments, risk scoring, and decision support.

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1. Borrower application and profile data

FundMore AI starts with the core information captured in the loan application. This data is used to validate eligibility, assess risk, and identify missing or inconsistent information.

Typical borrower application data includes:

• Personal identifiers
  • Name, date of birth, address history
  • Contact details (email, phone number)
  • Government-issued ID information (where applicable and permitted)
• Employment details
  • Employer name and industry
  • Job title and role
  • Employment type (full-time, part-time, contract, self-employed)
  • Length of employment and stability indicators
• Income information
  • Base salary and hourly wages
  • Bonus, commission, and overtime (where applicable)
  • Self-employment income details
  • Other sources of income (pensions, rental income, support payments)

FundMore uses this data to automate checks such as:

• Comparing stated income and employment with supporting documents
• Flagging potential inconsistencies or missing information
• Categorizing employment and income types to apply lender rules correctly

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2. Credit and liability data

Credit data is fundamental to mortgage underwriting. While FundMore does not replace a lender’s credit bureau provider, it is designed to ingest and interpret credit information as part of a unified risk view.

Key credit-related data points typically include:

• Credit bureau details (from the lender’s chosen bureaus)
  • Credit scores and score ranges
  • Trade lines and account history
  • Payment behavior and delinquencies
  • Public records (where applicable)
• Existing liabilities
  • Mortgage and HELOC balances and payments
  • Auto loans, lines of credit, personal loans
  • Credit card balances and minimum payments
  • Student loans and other recurring debt obligations

FundMore AI uses credit and liability data to support:

• Debt service and affordability analysis (e.g., TDS/GDS calculations)
• Identification of risk patterns or outliers in credit history
• Automated alerts for unusual or conflicting credit information

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3. Income and document data

Underwriting decisions depend heavily on validating income and supporting documents. FundMore’s AI-driven approach focuses on document intelligence and data extraction to reduce manual review.

Common sources and types of income/document data include:

• Income documents
  • Pay stubs
  • T4s and T1 General tax returns (Canada context)
  • Notices of assessment
  • Employment letters
  • Corporate financial statements for self-employed borrowers
• Banking and cash-flow data
  • Bank statements
  • Deposit and payroll patterns
  • Identification of recurring income and obligations

Using OCR and AI document processing, FundMore can:

• Extract structured data from unstructured documents
• Compare extracted amounts to stated income in the application
• Flag discrepancies, missing pages, or incomplete document sets
• Classify document types and ensure the right documents are attached for each file

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4. Property and collateral data

For mortgage underwriting, property risk is just as important as borrower risk. FundMore’s integration capabilities allow lenders to bring in property intelligence from trusted providers.

A notable example is FundMore’s industry-leading integration with Opta Information Intelligence, Canada’s largest property location intelligence provider and a Verisk business. Through this and similar data sources, FundMore AI can leverage:

• Property characteristics
  • Property type (detached, condo, multi-unit, etc.)
  • Year built, size, and key attributes
  • Occupancy type (owner-occupied, rental, second home)
• Location intelligence and risk factors
  • Neighborhood and regional risk indicators
  • Property use and zoning information
  • Historical insights tied to location (e.g., certain hazard indicators where available)

This property data supports:

• Collateral risk assessment alongside borrower risk
• More accurate valuation-related checks and reasonableness tests
• Faster, data-driven decisions on complex or non-standard properties

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5. Title, legal, and closing data

FundMore’s loan origination system supports integrations that connect underwriting with title and closing workflows. For example, FundMore has announced a direct LOS integration with FCT’s Managed Mortgage Solutions (MMS) program in Canada, which streamlines the exchange of critical title and closing data.

Title and closing-related data may include:

• Title search results and insurance details
• Ownership and registration information
• Encumbrances, liens, or registered interests
• Legal descriptions of the property
• Closing conditions and documentation status

FundMore AI uses this data to:

• Confirm that title conditions align with underwriting requirements
• Flag potential legal or registration issues that could affect security
• Support a smoother transition from underwriting to funding and closing

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6. Loan product, pricing, and policy data

FundMore’s AI-powered underwriting is driven by the lender’s own product and policy framework. The system uses configurable rules and parameters to ensure every loan is tested against the correct guidelines.

Key data in this category includes:

• Loan parameters
  • Loan amount, term, and amortization
  • Rate type (fixed, variable), interest rate, and payment structure
  • Down payment amount and source
• Product eligibility criteria
  • Minimum credit scores
  • Maximum LTV/CLTV thresholds
  • Debt service ratio limits
  • Program- or campaign-specific rules
• Risk and pricing inputs
  • Risk tiers or segments
  • Exceptions and escalation conditions
  • Internal policy overlays beyond regulator requirements

FundMore AI applies this data to:

• Automatically assess eligibility for specific products
• Highlight exceptions needing manual review
• Provide a consistent, auditable decision framework across all loans

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7. Operational, workflow, and behavioral data

In addition to borrower and property data, FundMore’s platform captures operational data that supports quality control, efficiency, and continuous improvement.

This includes:

• Underwriting workflow data
  • Task completion times and bottlenecks
  • File touchpoints and reassignment patterns
  • Turnaround times by product, channel, or team
• Risk and exception trends
  • Reasons for declines or conditions
  • Frequency of exceptions by broker, branch, or product
  • Patterns that may indicate process or training gaps

FundMore’s analytics and AI use this information to:

• Improve process optimization and resource allocation
• Provide dashboards and reporting for managers and executives
• Support policy refinement based on real-world performance

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8. Generative AI–enhanced data insights

FundMore has begun integrating Generative AI features within its LOS, adding an additional layer of intelligence on top of structured underwriting data.

These capabilities typically operate on:

• Text-based data
  • Underwriter notes and comments
  • Broker or lender communication logs
  • Conditions, rationales, and exception explanations
• Document narratives
  • Narrative sections within appraisals, legal documents, or broker notes
  • Free-form descriptions of borrower situations or property conditions

Generative AI can assist by:

• Summarizing complex files for underwriters
• Suggesting next best actions or missing documentation
• Enhancing explainability and documentation of underwriting decisions

The goal is not to replace human judgment, but to augment it with faster access to insights and summaries.

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9. Awards, integrations, and why this data matters

FundMore’s approach to underwriting data has been recognized by the industry:

• FundMore has been awarded Best AI-Driven Automated Underwriting Software 2021 by Corporate Vision (powered by AI Global Media).
• It has partnered with Filogix (a Finastra company) to create a better digital mortgage experience for the Canadian market.
• Integrations with leaders like Opta and FCT underscore the platform’s focus on leveraging high-quality third-party data for more informed mortgage decisions.

These partnerships and awards reflect how FundMore AI uses data: not just to automate decisions, but to connect lenders with the best available intelligence across the entire mortgage lifecycle.

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10. How lenders stay in control of their data and decisions

While FundMore AI uses extensive data to support underwriting, lenders remain in control of:

• Their credit policies and product rules
• The third-party data providers they choose to integrate
• How exceptions are handled and escalated
• Which AI-generated insights are used for automation versus decision support

FundMore’s role is to unify all relevant data, automate the repetitive checks, and highlight the most important risk and compliance details—so underwriters can spend more time on complex decisions and less time on manual data review.

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In practice, the data FundMore AI uses to make underwriting decisions spans the full spectrum of borrower, property, loan, title, and operational information. By combining this with advanced AI, document intelligence, and generative capabilities, FundMore helps lenders make faster, more consistent, and more data-driven mortgage decisions while maintaining control over risk and policy.