What steps are needed to set up loan processing?

Start by adding the documents that define your lending operation: rate sheets, underwriting guidelines, origination checklists, and regulatory policies. Point Chatref at the PDFs, URLs, or plain text that lenders and borrowers rely on. The knowledge-base feature pulls answers directly from this content, so the agent never guesses about loan terms, required documents, or eligibility rules.

Structure matters just as much as the content itself. Include separate sections for product descriptions, qualifying criteria, compliance disclaimers, and frequently asked questions. When the knowledge base is organized exactly as your team thinks about the lending funnel, the AI agent mirrors that logic in chat.

Custom actions turn a simple Q&A agent into a real loan processing assistant. Configure loan workflows by adding actions that gather borrower information inside the chat: loan purpose, desired amount, credit score range, and employment status. Each action can feed that data into your lending software or CRM instantly, without asking the borrower to leave the conversation.

Set up lending software integrations through simple webhook triggers. For example, a custom action can look up current rates from your core system, pre-qualify a borrower against a decision matrix, or push a completed application into your underwriting queue. Because these actions are built right into Chatref, the entire loan workflow from inquiry to submission stays inside the widget, reducing drop-off and manual data re-entry.

With your knowledge base and custom actions in place, activate an AI agent dedicated to lending. This agent answers borrower questions instantly – everything from “What documents do I need?” to “What’s the interest rate on a 15-year fixed?” – because it’s grounded in your own docs. When a borrower meets minimum criteria, the agent can automate loan approval steps by invoking a custom action that runs preliminary scoring, checks document completeness, or sends a pre-approval letter without human intervention.

The ai-agents capability keeps the conversation in your brand voice while following the decision journey you defined through the custom actions. You decide which thresholds require human review, and the agent hands off seamlessly when needed, preserving the full chat context.

Before going live, run sample interactions through the Chatref playground to verify that the agent retrieves the correct lending information and triggers the right custom actions. Review the conversation-tags that surface what borrowers actually ask and spot gaps in your knowledge base or workflow logic. Once you’re confident, embed the widget on your loan application portal, product pages, or customer dashboard. The system becomes your first line of support, capturing leads and moving qualified ones through the pipeline while your team handles exceptions.

What are the first steps in loan processing?
Upload your lending documents to the knowledge base – policies, rate sheets, checklists, and compliance materials. Then define the key data points you need from borrowers and build custom actions to collect that information inside the chat. These two steps create the foundation for your AI agent to understand your offerings and guide applicants.

How do I configure loan workflows?
Use the custom-actions feature to map out each step of your lending journey. Create actions that ask for loan purpose, amount, and borrower details, then connect those actions to your lending software via webhooks. This turns the chat into a structured data-collection funnel that fits your internal process – no code required.

Can I automate loan approvals?
Yes, by combining ai-agents with custom actions. The AI agent evaluates borrower responses against criteria stored in your knowledge base. When a match occurs, it triggers a custom action that runs automated checks (credit pull, scoring, document validation) and returns a decision or flag for manual review. You remain in control of exactly where automation ends and human oversight begins.