Bank Statement Analysis Software for Lenders

Upload any bank statement and get the metrics that drive the decision: income, cash flow, NSF activity and existing debt.

Live demo, analyze a statement free, no signup

PDF, JPG, PNG, BMP, HEIC, TIFF

Upload a document to extract

256-bit encryption
GDPR
Auto data purge

Built for lenders, MCA funders, brokers and credit teams

PDF statements
Scanned & photo
XLSX export
CSV export
JSON via API
Batch upload

The underwriting snapshot

Every metric a credit decision needs

Computed deterministically from every extracted transaction, not estimated from summary pages. Every figure traceable to its source line.

Recurring income

Deposits grouped into income streams with estimated monthly amounts

Monthly cash flow

Deposits vs withdrawals and net flow, month by month

Average daily balance

Carried forward day by day across the full period

NSF & overdrafts

Every incident counted, fees totaled, dates listed

Existing loan payments

Debits to other lenders and funders, grouped by creditor

Negative balance days

Days below zero, a direct cash-stress signal

Largest deposits

Biggest credits with dates, concentration flagged

Automatic risk flags

Red and yellow flags your analysts review in seconds

How it works

From statement PDF to decision in three steps

No templates per bank, no manual spreading. Upload and the AI does the reading and the math.

01

Upload statements

Drop in PDFs, scans or photos, one statement or a multi-month package, from any bank or credit union.

02

AI extracts & analyzes

Every transaction is extracted and verified, then cash flow, income streams, NSF activity and debt payments are computed.

03

Decide with confidence

Read the underwriting snapshot, download the Excel report, or pull structured JSON into your LOS via API.

Built for lending teams

The whole borrower file, not just statements

28 lending document types extracted out of the box, pay stubs, W-2s, tax returns, P&Ls, debt schedules, credit reports, applications, so you can assemble the full picture of an applicant's financial situation.

Any bank, any format
AI reads statements from every bank and credit union, native PDFs, scans and photos, with no per-bank templates.
Income verification
Recurring deposits detected and cross-checkable against pay stubs, W-2s and tax returns processed in the same platform.
Statement verification
Balances recomputed from the ledger; mismatches and suspicious deposit patterns flagged for review.
API & webhooks
POST statements, receive the full metrics object as structured JSON, straight into your LOS or decision engine.
POST /v1/extract
{
  "document_type": "bank_statement",
  "status": "completed",
  "metrics": {
    "estimated_monthly_income": 18540.00,
    "avg_daily_balance": 11072.40,
    "net_cash_flow": 4945.00,
    "nsf_count": 0,
    "negative_balance_days": 0,
    "monthly_debt_payments": 1260.00
  },
  "transactions": [
    { "date": "2026-05-02",
      "description": "PAYROLL DEPOSIT",
      "credit": 6180.00 }
  ]
}

Security & compliance

Built to be trusted with borrower data

Bank-grade security, granular access controls and the audit trail your compliance team expects from a lending vendor.

No attestation claimed

We hold no SOC 2 attestation or ISO 27001 certificate today and will not imply otherwise. Security documentation is available on request.

End-to-end encryption

TLS 1.2+ in transit and AES-256 at rest for every document and report.

GDPR & data residency

Data-subject controls and residency options for regulated lenders.

Zero retention option

Borrower documents purged automatically after extraction.

SSO / SAML & SCIM

Enterprise identity, provisioning and role-based access control.

Full audit logs

Every upload, view and export logged for examiner-ready trails.

What every statement analysis includes

26
Underwriting metrics per statement
5
Automated risk flags
XLSX / CSV
Export formats
25
Pages per statement on Starter

Check the work

Every number traceable to a transaction

You should not take an underwriting figure on faith, so the product is built so you never have to.

Full transaction ledger

Every metric is computed from the extracted transaction list, which you can read line by line next to the original statement page.

Deterministic math

Totals, averages, balances and counts are computed in code from those transactions, not estimated by a language model.

Export and re-check

Download the full ledger and metrics as XLSX or CSV and reconcile them against the statement in your own spreadsheet.

Make your next lending decision on verified data

Analyze your first statements free. Plans from $99/month, 50% off billed annually.

FAQ

Frequently asked questions

What does LenderAnalyzer do?

It analyzes borrower bank statements for lending decisions: AI extracts every transaction, then computes underwriting metrics, recurring income, monthly cash flow, average daily balance, NSF/overdraft activity, negative balance days and existing loan payments, into a snapshot and downloadable report.

Which banks and formats are supported?

All of them. The AI is format-agnostic, so statements from any bank or credit union work, native PDFs, scans and photos, with no per-bank templates to configure.

Can it verify income?

Yes. Recurring deposits are grouped into income streams with estimated monthly amounts, and you can cross-check against pay stubs, W-2s, 1099s and tax returns extracted in the same platform.

Does it detect existing loans and stacking?

Debits matching loan, financing and merchant-advance patterns are grouped by creditor with estimated monthly totals, so existing obligations and stacked advances surface immediately.

Is there an API?

Yes. Submit statements via REST API and receive the full metrics object plus transactions as structured JSON, with webhooks on completion, built to feed an LOS, CRM or decision engine.

How is borrower data secured?

Documents are encrypted in transit and at rest, encrypted at rest, with optional automatic purge after extraction, SSO and full audit logs on enterprise plans.

Resources

From the blog