Document review for loans, underwriting and contracts
In short
A document review agent reads loan files, insurance underwriting submissions and contracts, extracts the fields your policy needs, and checks them against your rules. Clean files move to the next step on their own; anything ambiguous, out of range or incomplete goes to a reviewer with the extracted fields and the source page attached. Built for lending, underwriting and contract operations teams that process high volumes of similar documents.
The problem
Lending, underwriting and contract teams read the same kinds of documents by hand every week: application packages, bank statements, broker submissions, signed contracts. The work is high volume and rules-heavy, which makes it exactly the kind of task where a person's attention drifts and a rule gets applied inconsistently from file to file. Outsourcing the reading does not remove the risk. What is needed is software that applies every rule the same way, every time, and still asks a person before anything gets approved, declined or priced.
What it moves
- Reviewer time per file
- How long a person spends on a file that reaches manual review.
- Exception rate
- The share of files that need a person, by reason.
- Cycle time
- Time from intake to a decision, across the full file volume.
- Policy consistency
- Whether the same rule was applied the same way across reviewers and shifts.
How the agent works
Step 1
Document arrives
A file enters through an upload, an inbox integration or a case management system, as a PDF, scan or image.
Step 2
Parse to text
Dynamiq converts the document to text with a vision-capable model, handling scans and multi-page files the same way as native PDFs.
Step 3
Screen before extraction
PII and prompt injection detectors run on the extracted text before any reasoning model sees it, and flagged documents route straight to manual review.
Step 4
Extract structured fields
The clean text is extracted into a fixed schema (applicant, amounts, dates, clauses) so downstream systems receive structured data, never prose.
Step 5
Check against policy
The agent checks the extracted fields against your credit, underwriting or contract policy, grounded in a knowledge base of your own rules.
- Human review
Step 6
Exceptions to a reviewer
Anything ambiguous, out of policy range or incomplete is flagged to a reviewer with the extracted fields and the source page, instead of being approved automatically.
Controls
Deploy on Dynamiq Cloud or self-host in your own AWS, Azure, GCP, IBM Cloud, OpenShift or Kubernetes environment, so extracted documents never have to leave infrastructure you control. Our engineers build the extraction schema and policy checks with your lending, underwriting or legal team before any file skips manual review.
- Rules your experts own
- Credit policy checks run as decision tables and rules your underwriters can read and edit, with saved test cases that show what each rule covers.
- Detectors before the model
- PII and prompt injection are classified on the extracted text before it reaches the reasoning model, and the verdict is recorded on every run.
- Structured output only
- The agent's answer is forced into a fixed JSON schema, so a downstream system never has to parse free text.
- Reviewer sign-off on exceptions
- Files outside policy range or missing required fields pause for a person, not an automatic approval or decline.
- Project-level scoping
- The workflow, connections and knowledge base live in a private project, open only to the people who build or operate it.
- Full audit trail
- Every run is traced and replayable, including which page a field was extracted from and which policy passage the agent cited.
Systems it connects to
- Loan origination and underwriting systems, over API
- Contract and document storage (SharePoint, Box, Google Drive, Dropbox)
- E-signature (DocuSign)
- Case and workflow management (Jira, ServiceNow)
- CRM (Salesforce)
- Credit, underwriting and contract policy knowledge base
- Document parse and extract through the AI Gateway
- Core record systems, over API
Built with
- Models and AI GatewayUse 29 model providers from the SDK, call the most-used models behind one endpoint and one credential, or host and fine-tune open models yourself.
- Agent BuilderDesign an agent on a visual canvas or in the open-source Python SDK. Both compile to the same engine, so what you build ships either way.
- Guardrails and approvalsDetect PII and prompt injection before a model sees them, enforce content policies, and pause tool steps for a person to approve.
- KnowledgeA knowledge base converts your documents into searchable context: ingestion, chunking, embedding and storage, managed for you or pointed at your own vector store.
Industries
- Financial servicesBack-office automation, customer service by phone and chat, and KYC and AML work, on one governed platform your compliance team can audit.
- InsuranceAnswer policyholders by phone and chat, prepare claims and underwriting files, and keep an adjuster or underwriter approving every decision.
- Government and public sectorCitizen services, benefits and permit review, and program reporting, with a person in the loop and your agency in control of the infrastructure.
- HealthcarePatient intake, referral and document review, and clinical knowledge assistants, with PII and prompt injection screened before a model reads anything.
Questions and answers
Does the agent approve or decline files on its own?
No. It extracts and checks fields against your rules, and anything ambiguous or out of range goes to a reviewer. Approval and decline stay a human decision.
What document types does it handle?
PDFs, scanned images, Word files and other common formats, converted to text with a vision-capable model before extraction.
Can it review insurance underwriting submissions?
Yes. It extracts the risk details from a submission and its supporting documents into your schema, checks them against your appetite and guidelines, and flags exceptions to an underwriter.
Can it screen for PII before a model reads the document?
Yes. PII and prompt injection detectors run on the extracted text before any reasoning model sees it, and the platform detects and flags rather than rewriting the text.
How do you handle documents from different states or jurisdictions?
The extraction schema and policy knowledge base are yours to define, so rules can vary by product, region or jurisdiction within the same workflow.
What about insurance claims?
Claims run as their own flow, from first notice of loss by phone or chat through document review to adjuster sign-off, described on the insurance claims processing page.
See this agent on your data.
Talk to our team. We will walk through how it works in your environment, with your systems.