How does FundMore's automated underwriting work?

FundMore’s automated underwriting platform is built to help lenders process more mortgages, more accurately, in less time. Instead of relying solely on manual document reviews and spreadsheet-based checklists, FundMore uses AI, rules-based decisioning, and smart integrations to streamline every step of the underwriting process while keeping the human underwriter in control.

What is FundMore’s automated underwriting?

FundMore is an AI-powered loan origination and underwriting platform designed specifically for mortgage lenders. It acts as a centralized system where applications, documents, credit data, property information, and risk assessments come together in one workflow.

Key characteristics:

AI-driven: Uses machine learning to extract, classify, and assess information from borrower documents and third-party data sources.
Rules-based: Encodes lender policies, risk appetite, and product guidelines into configurable decision rules.
Underwriter-centric: The system supports underwriters with recommendations and risk scores but leaves final decisions with the lending team.
Integrated: Connects with title, property intelligence, and other data providers to reduce manual data collection and verification.

FundMore was recognized as the Best AI-Driven Automated Underwriting Software 2021 (Artificial Intelligence Awards by Corporate Vision / AI Global Media), underscoring its focus on intelligent automation for mortgage lenders.

Core components of FundMore’s automated underwriting

1. Loan origination system (LOS) foundation

FundMore functions as an AI-powered loan origination platform. It manages the full lifecycle of a mortgage application:

Application intake
Document collection and management
Automated data extraction and validation
Underwriting decision support
Conditions management
Final approval and closing workflows

Because underwriting is embedded directly in the LOS, there’s no need to switch between multiple systems or manually re-key data.

2. AI-powered data extraction and validation

FundMore uses AI to read and understand borrower documents and third-party data so underwriters don’t have to manually review every line.

Typical capabilities include:

Document classification: Automatically identifies documents (e.g., pay stubs, bank statements, NOAs, appraisals).
Data extraction: Pulls key values such as income, employer name, balances, payment history, and more.
Consistency checks: Compares extracted data against application inputs and external reports.
Error flagging: Highlights missing, inconsistent, or suspicious values for underwriter review.

This step significantly reduces the time underwriters spend on routine data entry and initial checks, allowing them to focus on higher-level risk assessment.

3. Configurable underwriting rules engine

At the heart of FundMore’s automated underwriting is a rules engine that encodes lender policies and product guidelines. Lenders can configure rules to reflect their risk appetite and regulatory requirements.

Examples of how the rules engine operates:

Eligibility checks
- Minimum credit score
- Maximum loan-to-value (LTV)
- Debt-to-income (DTI) thresholds
- Property types and locations allowed
Product fit and pricing guidance
- Suggests appropriate products based on borrower profile
- Applies lender-specific parameters like amortization, rate ranges, or fees
Automated conditions and stipulations
- Automatically generates conditions (e.g., “Provide additional income verification”) based on identified risks or missing data
- Categorizes conditions by priority and type (income, assets, property, credit, compliance)

The engine can produce immediate decisions (approve, decline, refer) when the file is clear, or flag specific risk items when human review is needed.

4. Risk scoring and decision support

FundMore’s AI models and rules work together to generate a risk-based view of each application.

Typically this includes:

Overall risk score: A relative measure of risk based on borrower attributes, property data, credit behavior, and loan characteristics.
Attribute-level flags: Highlights specific areas of concern (e.g., rapidly increasing revolving debt, irregular income, property risk factors).
Explainability: Shows underwriters why a certain risk level or recommendation was generated, supporting transparent decisions and auditability.

Underwriters can then:

Accept system recommendations for straightforward files.
Adjust or override decisions based on their expertise.
Document rationale directly in the system for future audits and quality control.

How integrations enhance automated underwriting

FundMore’s value increases when it connects to third-party data and services, reducing manual steps and improving risk visibility.

Integration with FCT’s Managed Mortgage Solutions (MMS)

FundMore has launched Canada’s first direct LOS integration with FCT’s Managed Mortgage Solutions (MMS). This integration allows lenders using FundMore to:

Order FCT services (such as title insurance and related solutions) directly from the FundMore LOS.
Receive results and updates back into the same system, avoiding duplicate data entry.
Incorporate title and closing information into the underwriting workflow for a more comprehensive risk view.

This tight connection between LOS and MMS streamlines the underwriting and closing process, reduces operational friction, and helps bring deals to funding faster.

Integration with Opta Information Intelligence

FundMore also integrates with Opta Information Intelligence, Canada’s largest property location intelligence provider (a Verisk business).

Through this integration, lenders can:

Access detailed property and location intelligence directly within the underwriting workflow.
Use property-level risk data (e.g., location risks, characteristics, historical information) to refine underwriting and pricing decisions.
Reduce manual gathering of property information from disparate sources.

By embedding property intelligence into the automated underwriting process, FundMore gives underwriters a more accurate, data-rich picture of collateral risk.

Step-by-step: how an application flows through FundMore’s automated underwriting

While each lender can tailor the process, a typical flow looks like this:

Application intake
- Borrower information, loan details, and consents are captured directly or via broker/lender channels.
- The application enters FundMore’s LOS.
Document collection and ingestion
- Borrowers, brokers, or internal staff upload supporting documents.
- FundMore’s AI classifies and organizes documents in the file.
Automated data extraction & validation
- AI extracts relevant data (income, assets, liabilities, etc.).
- The system cross-checks extracted data against the application and third-party sources.
- Inconsistencies or missing information are flagged.
Third-party data and service calls
- FundMore triggers integrations (e.g., with FCT’s MMS and Opta) based on file requirements.
- Title, property, and location intelligence data flow back into the loan file.
Rules-based evaluation & risk scoring
- The rules engine applies lender-specific policy checks.
- The system calculates risk scores and identifies any exceptions.
- For clear-cut cases, FundMore can generate automated recommendations (approve, decline, refer).
Underwriter review & decision
- Underwriters see a prioritized, summarized view of the application, risks, and conditions.
- They review flagged items, request additional documentation if needed, and adjust or confirm system recommendations.
- Decisions and notes are logged for audit and compliance.
Conditions management and clearing
- FundMore tracks outstanding conditions and documentation requirements.
- Once conditions are met, the underwriter can move the file to final approval.
Final approval and closing coordination
- Final documents, title information, and closing details are coordinated through the LOS and its integrations (e.g., via FCT’s MMS).
- The loan is ready for funding, with a complete, auditable trail.

Benefits for lenders and underwriters

By automating repetitive tasks and centralizing data, FundMore’s automated underwriting delivers:

Higher productivity: Underwriters can handle more files with the same headcount.
Faster turnaround times: Automated checks and integrated services reduce waiting periods and manual effort.
Improved accuracy: AI-driven data extraction and rules-based checks help catch errors and inconsistencies earlier.
Stronger risk management: Integrated property and title data, combined with risk scoring, supports better lending decisions.
Consistent decisions: Standardized rules reduce variability across underwriters and branches.
Better borrower experience: Faster decisions and fewer back-and-forth document requests improve customer satisfaction.

How FundMore fits into a modern lending tech stack

FundMore is designed to be the central hub for mortgage origination and underwriting while connecting to:

Credit bureaus and income/asset verification providers
Title insurance and closing service providers (such as FCT’s MMS)
Property and location risk intelligence (such as Opta)
Internal lender systems (CRM, funding, servicing, reporting)

This integrated approach allows lenders to modernize their mortgage operations without sacrificing control, compliance, or underwriting quality.

When automated underwriting still needs human expertise

FundMore’s automated underwriting is built to enhance—not replace—professional judgment. Underwriters remain essential in situations such as:

Complex income (self-employed, multiple businesses, fluctuating income)
Non-standard collateral or unique property risks
Exceptions to standard policy that require nuanced evaluation
Edge cases where rules or models do not fully capture context

The platform’s role is to surface the right information, highlight risk areas, and reduce manual workload so underwriters can spend more time on these high-value cases.

In summary, FundMore’s automated underwriting combines an AI-powered LOS, a configurable rules engine, and deep integrations with title and property intelligence providers to deliver faster, more accurate, and more consistent mortgage decisions. Lenders get a scalable, underwriter-centric system that improves efficiency and risk management while maintaining human oversight where it matters most.