The Credit Union Board's Briefing on AI in Lending
AI Agents approve and deny loans based on applicants’ data and current market conditions, the same way a human underwriter would, only 20x faster. AgentFlow reads the full loan file, verifies the data, and returns a decision your team can trust.
Automated underwriting uses AI agents to read a loan file, verify the applicant’s financial data, and reach a loan decision in seconds. AgentFlow automates mortgage underwriting end-to-end, cutting processing costs while keeping underwriters in control of the final loan decision.
At Libra Internet Bank, we see AI not as an experiment, but as a core driver of growth and efficiency. Our work with Multimodal confirmed that Agentic AI can move beyond isolated use cases into real, production-grade impact across banking workflows.
Libra Internet Bank
Nobody is doing what we’re doing with Multimodal, not even close.
Jim Beech — CEO, Direct Mortgage Corp.
Most automated underwriting systems need lengthy supervision. AI Agents have advanced capabilities that make them almost fully independent, so your underwriters manage the whole lending process from a single screen.
AI Agents analyze hundreds of data points to make the lowest-risk loan decisions for every mortgage application.
Agents follow relevant regulations such as TILA, fair lending, and Regulation B, and maintain auditable trails, so every underwriting decision stays examiner-ready.
Stop fraud before it occurs. AI Agents detect data discrepancies for fraud detection with unmatched speed.
AI Agents learn from your data and feedback around the clock, so accuracy improves over time.
AI Agents can analyze any document you choose to assess a borrower’s creditworthiness accurately.
Our underwriting automation software connects to the core, loan origination, and document management systems your team already uses, so there is no rip-and-replace.
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Manual underwriting can take days because a human underwriter checks each document by hand. Automated underwriting returns a loan decision in seconds and applies the same criteria to every file — and that is where the largest gains sit.
| Factor | Manual underwriting | Automated underwriting |
|---|---|---|
| Time to decision | Days | Seconds |
| Processing costs | High | Up to 40% lower |
| Consistency | Varies by reviewer | Uniform across all applications |
| Volume | Limited by staff | Unlimited applications at once |
| Human error | Higher | Reduced |
AI Agents automate the mortgage underwriting workflow from intake to decision.
Intelligent document processing pulls financial data from large volumes of documents — loan applications, bank statements, pay stubs, and tax forms — into the exact fields you need, with no manual data entry. We train the agents on your schema before you adopt them, so income verification and document processing run automatically.
Automatically verify data, then analyze the borrower’s credit history, credit score, debt-to-income ratio, and the loan amount to make minimum-risk loan decisions. Machine learning weighs hundreds of data points and historical data, then generates automated reports that explain each decision and support regulatory compliance.
Get natural-language answers from your own systems, giving staff a chat-like way to check a file.
Automated underwriting makes the biggest difference on high-volume files. By removing repetitive tasks and administrative burden, AgentFlow improves operational efficiency at every step and elevates the customer experience with faster loan approval, which shortens time to close and can reduce closing costs across your lending services.
Automated underwriting must satisfy fair lending requirements. Models cannot use race or gender in credit decisions, and disparate impact testing checks how the system treats protected groups. Every declined application comes with explainability, so a lender can give a clear reason for each underwriting decision, and automation maintains an auditable trail that an examiner can follow.
Algorithmic bias can occur if the data used to train a system is biased, so models are tested and monitored. A human underwriter reviews referred cases, maintaining human judgment and manual review in any approval decision that affects a customer.
Your team clones the playbook that matches the file, runs it on real documents, and gets a structured, examination-ready result. Every playbook runs the same four steps, so each loan is handled to the same standard.
The full library is 75+ lending and operations playbooks; this page features the Residential Mortgage set.
Here is what makes them different from any other automated underwriting system.
Large language models and artificial intelligence power AI Agents, so they mimic human thinking and interact in natural language.
We train AI Agents on your documents, data, and tasks, so they master your workflow before you adopt them.
Manage AI Agents through a chat interface, connect them with your databases, and keep every step tracked and auditable.
The same agents handle business loan underwriting for banks, auto loans, and other consumer lending. They also support private equity firms across diligence, fund, and portfolio operations — reading data rooms and producing automated reports that streamline diligence and speed up business decisions.
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