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Use case · Bank statements

Bank statement extraction. Transaction by transaction.

Cash-flow underwriting, AML, expense automation, and small-business lending all depend on bank statements being parsed accurately, transaction by transaction. fluex extracts every transaction, balance, and account-metadata field from US bank and credit-card statements in under three seconds — with category inference and per-line confidence.

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Why bank statements are hard

Every US bank formats statements differently. Chase, Bank of America, Wells Fargo, Capital One, Citi, US Bank, and the regional banks each have their own layout, transaction formatting, and balance presentation. Add credit-card statements and the format space doubles. Cash-flow underwriting needs every transaction parsed; OCR-based parsers hit 70-80% transaction accuracy and miss balances on multi-page statements.

How fluex does it

fluex parses statements semantically: it identifies the institution, finds the statement period, walks every transaction, normalizes amounts and dates, and reconciles ending balance against the transaction sum. Multi-page statements are stitched automatically; credit-card statements are handled with the same schema as bank statements. Transaction categories (deposit, withdrawal, ACH, wire, fee, interest) are inferred from descriptors.

Sample extraction output

doc_typeBank statement
institutionJPMorgan Chase
account_holderJordan T. Hall
account_number****6789
statement_period2026-03-01 to 2026-03-31
opening_balanceUS$ 18,420.55
total_depositsUS$ 12,450.00
total_withdrawalsUS$ 9,234.18
ending_balanceUS$ 21,636.37
transactions47 transactions parsed
balance_check✓ deposits − withdrawals = balance change
avg_daily_balanceUS$ 19,820.40
nsf_count0
confidence0.98 → auto-approved

What you get out of the box

Every US bank, every credit card

Chase, BoA, Wells, Capital One, Citi, US Bank, regionals, plus Visa / Mastercard / Amex statements.

Transaction-level confidence

Each transaction is returned with a confidence score, source page reference, and bounding box.

Multi-page stitching

30-page statements are stitched into one logical document with transaction continuity preserved.

Balance reconciliation

Opening + deposits − withdrawals = ending. Inconsistencies are flagged before they hit your underwriting model.

Integration patterns

For lenders, fintechs, and expense-automation platforms, fluex's async mode handles 12-month statement bundles in under 60 seconds. Direct integrations exist for Plaid (as a fallback when transaction-only data isn't enough), Brex, Ramp, and major lender CRMs. The REST API drops normalized transaction data straight into your cash-flow model.

Compliance & trust

Bank statements contain account numbers and transaction patterns. fluex masks account numbers in audit metadata by default and retains documents encrypted at rest with per-tenant keys. Configurable retention from 0 to 7 years. See our trust page for the full posture: encryption, tenant isolation, sub-processors, GDPR DPA, CCPA, SOC 2 Type II in progress, and HIPAA BAA on Enterprise.

Get started

Pay-per-page pricing means you can start an evaluation today without an annual commit. Most teams ship their first bank-statement extraction into production within a week.