AI for Finance & Underwriting

Betsy is Better's voice-based AI loan assistant, built on generative AI and large language models to guide borrowers from pre-approval through closing.

AI underwrites lease insurance policies for rental housing, scoring property and renter risk from property, financial, claims, and geographic data to price coverage and forecast rent loss.

Built by Fundrise, RealAI pairs large language models with big-data machine learning, trained on trillions of data points, to underwrite, screen acquisitions, and generate investor-ready analyses across U.S. residential and commercial properties.

Ocrolus classifies financial documents and extracts data using OCR, computer vision, and machine learning, then flags fraud and routes edge cases to human reviewers. Ocrolus reports coverage of 1,600+ document types at 99+% accuracy.

Blooma applies machine learning and OCR to automate commercial real estate underwriting, parsing deal documents and scoring risk across thousands of data points per deal.

Reads a sponsor's deal documents and answers lender underwriting questions, citing its sources.

Patented AI reviews mortgage closing documents to catch errors before funding, automating quality control across lender and title workflows. Snapdocs reports 99% accuracy in flagging document errors and review time cut from over 45 minutes to under 10 minutes per loan.

AI trained on multifamily documents reads rent rolls, T12 operating statements, and offering memos, then writes the extracted financial data straight into a user's existing Excel underwriting model.

OCR and custom NLP models read CRE documents and pull structured data into underwriting systems, with self-learning models that retrain on user corrections to handle institution-specific formats.

Parses a broker's uploaded CRE data (rent rolls, T12 financials, comps, underwriting models, photos) and pairs it with Google's Gemini research model to draft branded offering memorandums, broker opinions of value, and loan packages, with a quality-control team reviewing every deck before delivery.

Grades property condition from listing photos and forecasts rental income with neural networks.

JunieAI applies large language models to Juniper Square's private markets fund data, automating investor relations work so IR teams can capture activity, draft outreach, and prioritize LPs faster.

Agentic AI runs end-to-end mortgage origination through SOLO, a mortgage-trained model that reviews documents, extracts data, and checks underwriting compliance without manual handoffs.

Blend Autopilot, an agentic AI agent for mortgage and consumer lending, reviews borrower documents and application data and returns a completed origination review in roughly 15 seconds, which Blend reports versus a next business day turnaround.

Two AI systems run inside the underwriting model. A machine-learning forecaster assigns each lease a renewal probability and blends those odds with the property's own payment history to project income, recalculating automatically as the deal timeline advances. Saga, a natural-language assistant, answers questions about the model's assumptions, outputs, and scenario changes in plain English, grounded in the specific property.

Runs an accounting AI engine, HpAI, that pairs large language models with precision-trained accounting models.

Turns entire multifamily data rooms into structured, source-linked data for diligence audits.

Built AI uses machine learning to extract structured data from deal documents such as offering memos and rent rolls, then auto-generates cash-flow models and business plans in minutes, work that previously took weeks in Argus and Excel.

Janover Pro lets commercial real estate debt brokers describe a loan scenario in plain language, then triages millions of lender data points in seconds to match the deal against a verified originator database.

Kiavi prices and approves loans for residential real estate investors with proprietary machine learning models trained on its own lending data, including an after-repair-value model that scores fix-and-flip and rental deals in seconds.

V7 Go runs multimodal AI agents that read complex real estate documents and turn them into structured, downstream-ready data. The platform reasons across text, tables, visuals, and handwriting to abstract leases, analyze offering memorandums and loan documents, and build cash flow models from rent rolls. V7 reports 90 percent accuracy on lease data extraction and 70 percent less underwriting prep time.

Valuebase builds property valuations from an ensemble of nine independent machine learning models that value residential, commercial, and vacant parcels with land and improvements separated. It auto-enriches each parcel with 50+ derived features such as flood zones, topography, corner lots, and street frontage, and redraws neighborhoods from physical boundaries and market data. AI assistants then review appraisals: ValPal checks taxpayer appraisals for USPAP compliance and drafts appeal responses, RatioPal generates IAAO ratio studies (COD, PRD, PRB), and AgePal calculates consistent effective age across properties.

Decisional runs vector search over a firm's proprietary deal documents and pairs it with agentic writing, modeling, and charting. It produces investment memos, valuations, and cap tables, with every figure citing the exact source page. A spreadsheet-style canvas lets analysts run Python valuations and Monte Carlo simulations in the same workspace.

Pulse Real says its AI analyzes 200+ data points to generate a proprietary Pulse Score and ROI Score that rank market and neighborhood investment potential. Its Flipper AI tool reviews property images and data to estimate renovation priorities, costs, and projected returns.

Adema combines planning policy, geospatial data, and market demand into a reasoning layer that scores site viability, predicts planning approval likelihood, runs real-time development appraisals, and generates lender and investor ready reports.

Cactus parses CRE due diligence documents (rent rolls, T-12 statements, offering memoranda) and auto-builds five-year DCF and IRR models. The vendor claims over 99 percent parsing accuracy and runs 100-plus real-time audits to flag discrepancies between documents.

ClearAVM is a machine-learning automated valuation model that the company says covers 95% of US homes across 120M-plus properties, trained on closed-sale and refinance appraisal benchmarks and updated hourly. Clear Capital acquired computer-vision firm Restb.ai, which extracts property condition and features from photos at up to 99% accuracy and processes over 2 billion images monthly, to enrich valuation analysis.

Clik.ai uses machine learning and NLP trained on millions of CRE documents to extract and spread rent rolls, operating statements and T12s into production-ready Excel financial models, completing underwriting in minutes rather than hours. The vendor claims 99.9% extraction accuracy and roughly 70% time saved on underwriting workflows.

Finvise runs a hybrid model: a machine-learning valuation engine trained on 54 million-plus data points generates a value, reviewed and signed off by a RICS surveyor. The company reports the model lands within 10 percent of a human RICS valuation in 95 percent of cases (tested on 3,000-plus properties) and produces reports in roughly two seconds.

Graphite Note runs a no-code AutoML engine that automatically selects and tunes predictive and causal models to forecast outcomes like demand, churn, customer lifetime value, and price sensitivity, then layers prescriptive playbooks on top. For real estate and lending, it is used for predictive lead scoring and conversion forecasting on mortgage and property data, and its REST APIs let teams push datasets and pull predictions into existing systems.

PropMarker's AI advisor Lenah fuses listings, sold prices, EPC records, and land and planning data to surface deals, read floorplans, and score each opportunity by ROI and yield. The company cites an intelligence network of over 900,000 live listings, 16 million sold records, 34 million EPCs, and 200 million land and planning datapoints.

Alt-X is an AI agent that lives in Excel. Drop in an offering memorandum and it parses the document, extracts the key terms, and builds a full underwriting model: sources and uses, debt sized to LTV and DSCR constraints, ten-year cash flows, exit cap sensitivity, and a GP/LP waterfall. Every number is cited back to the exact page and line, and every change needs the analyst's approval before it is applied.

Deferred runs ARTE, an AI tax research assistant trained on 8,000-plus pages of US tax law that answers 1031 exchange and real estate tax questions with cited sources. The platform also uses AI to automate exchange documentation and deadline tracking.

Buildable runs AI agents that pull, structure, and analyze real estate data the way an analyst would. Upload a document or pick a site and its agents produce comparables, development feasibility, valuations, and market reports, then export to Excel and PDF.

Crunched is an AI agent that works inside Excel and PowerPoint to build, analyze, and verify financial models. It extracts data from documents like investment memos and rent rolls, builds underwriting models with linked formulas, and runs error checks across entire workbooks.

Capitalize runs AI agents that read loan documents, mortgage records, PDFs, and public filings to build borrower profiles, surface comparable loans, and match brokers with the right lenders. The agents automate skip-tracing and underwriting research, cutting tasks that took hours down to seconds.