CRE underwriting software that screens, sizes, and cites every number.

What CRE underwriting software should handle

A lender's underwriting workflow starts with messy deal intake: offering memoranda, rent rolls, operating statements, appraisals, third-party reports, term sheets, and public data lookups. The useful software layer is the one that turns those materials into structured, cited information without hiding the evidence.

Fynch AI is designed around that evidence model. Teams define required fields, document expectations, synonyms, review rules, and output templates once, then apply that standard to each property workspace.

• Test the request against your max LTV, min DSCR, min debt yield, and occupancy limits, and size the loan to the one that binds.
• Classify CRE source documents and extract underwriting fields into a shared data dictionary.
• Attach each fact to a source document, page, excerpt, public lookup, or vendor report.
• Identify missing critical fields, conflicting values, stale data, and low-confidence citations.
• Route exceptions to human reviewers before credit materials are generated.
• Draft credit memo outputs only from approved, citation-verified facts.

Why source evidence matters in CRE lending

The OCC describes CRE lending as covering acquisition, development, construction, and income-producing real estate financing. Those credit files depend on property-specific assumptions, borrower materials, collateral information, market context, and review history.

Interagency CRE concentration guidance emphasizes management information systems, market analysis, stress testing, credit underwriting standards, and credit risk review. Software that preserves citations and reviewer decisions helps make those controls easier to inspect.

How Fynch AI fits the underwriting workflow

Fynch reads the OM, rent roll, and T-12, reconciles the broker pro forma to the T-12, tests the deal against your credit box, and returns a screening memo in under 20 minutes. When a deal moves forward, it drafts the first credit memo from the same reviewed numbers.

Analysts work in one deal workspace where they can ask cited questions, compare values, approve or reject facts, and edit the memo, instead of copying unsupported numbers between files.

What to evaluate before buying

For CRE lenders, the buying question is not only whether a platform can parse PDFs. It is whether the system can support your credit policy, reviewer controls, data lineage, and deal-specific exceptions. That holds for banks, credit unions, life companies, CMBS lenders, debt funds, bridge lenders, and private credit funds alike.

• Can your team define its own field dictionary, required documents, units, and synonyms?
• Can it test a deal against your own limits, not a generic model?
• Can every extracted value be traced to a page, excerpt, lookup, or report?
• Can reviewers separate machine citation verification from human approval?
• Can the platform detect conflicts between borrower documents, public records, and vendor data?
• Can generated outputs be blocked until dependencies are approved?