Fintech · Lending · KYC

AI Bank Statement
Analysis &
Data Extraction

Q: What bank statement formats does the AI support?

PDF (scanned or digital/text-layer), Excel/CSV exports from any bank, MT940 / CAMT.053 SWIFT formats, OFX / QFX (Quicken) formats, and bank-specific XML exports. For scanned paper statements, we use multi-pass OCR with table detection to reconstruct the transaction grid — achieving 99%+ accuracy on standard A4 bank statement layouts from any country.

Q: What use cases does AI bank statement analysis support?

Credit underwriting: income verification, recurring salary detection, EMI obligation analysis, cash flow summary for NBFC/fintech lenders. KYC/AML: transaction pattern screening, PEP/adverse media linkage, round-tripping detection. Bookkeeping: automated categorisation of income and expenses, reconciliation against accounting software, month-end P&L feeds. Due diligence: financial health snapshot for M&A or investment assessment.

Upload any bank statement — scanned PDF, digital PDF, CSV, MT940, OFX — and get back structured transaction data, income summary, and anomaly flags in under 3 seconds. Any bank, any country.

PDF / Image OCRMT940 / CAMT.053OFX / QFXCSV / ExcelREST APIKYC / AML

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Request API Access

We'll set up a sandbox with your test statements. Usually ready same business day.

Use Cases

What You Can Build on Bank Statement AI

The structured output — transactions, categories, summary metrics — plugs directly into your credit, compliance, or accounting workflows.

💳

Credit Underwriting

For NBFC, fintech lenders, and embedded finance products. Verify income, detect recurring EMIs, assess cash flow health.

Recurring salary detection (date + amount pattern)
Monthly average balance calculation
Existing EMI / loan repayment detection
Bounce / return cheque flagging

🔍

KYC / AML Screening

For banks, NBFCs, and compliance teams. Screen transactions for unusual patterns, large cash flows, and round-tripping.

Large transaction alerts (configurable threshold)
Round-trip transaction pattern detection
Cash deposit / withdrawal concentration
Counterparty frequency analysis

📚

Bookkeeping Automation

Auto-categorise every transaction — rent, payroll, utilities, GSTIN payments, TDS — and push to QuickBooks, Xero, or Tally.

Category taxonomy (customisable per client)
GSTIN / vendor name extraction
Accounting software sync
Month-end P&L feed ready

🏢

Due Diligence

For PE / VC / M&A. 12-month financial health snapshot from bank statements when audited accounts aren't ready.

Revenue trend (inflows month by month)
Operating expense pattern
Key supplier concentration
Irregular transaction spikes

💰

Bank Reconciliation

For accountants. Match bank statement lines against accounting journal entries automatically — flag unmatched items for review.

Transaction → invoice matching
Partial payment handling
MT940 / CAMT.053 native parsing
95%+ auto-match rate

📱

Expense Management

Employee expense categorisation, policy breach detection (weekend spend, alcohol, non-approved vendors), manager approval queue.

Merchant category coding (MCC)
Per-policy rule engine
Duplicate claim detection
Export to SAP Concur / Expensify

API Output

Structured JSON in Every Response

Every statement processed returns the same clean structure — transactions array + summary metrics + anomaly flags.

{
  "account": {
    "bank": "HDFC Bank",
    "account_number_masked": "XXXX7842",
    "statement_period": "2026-06-01 to 2026-08-31"
  },
  "summary": {
    "total_credits": 485000.00,
    "total_debits": 312400.00,
    "avg_monthly_balance": 172600.00,
    "recurring_salary_detected": true,
    "estimated_monthly_income": 95000.00
  },
  "transactions": [
    { "date": "2026-08-01", "description": "SALARY ACME CORP",
      "credit": 95000.00, "category": "salary" },
    { "date": "2026-08-05", "description": "EMI HDFC HOME LOAN",
      "debit": 32000.00, "category": "loan_repayment" }
  ],
  "anomalies": [
    { "type": "large_cash_withdrawal", "date": "2026-07-22",
      "amount": 150000.00, "flag": "high" }
  ]
}

Pricing

Simple Per-Statement Pricing

Or fixed monthly for predictable volumes. Enterprise pricing for 10K+ statements/month.

Starter API

$8K – $20K

Setup + integration + 6-month support

REST API deployment
Up to 5,000 statements/month
PDF + CSV + OFX formats
Transaction extraction + categorisation
JSON output with confidence scores

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Common Questions

What bank statement formats does the AI support?

PDF (scanned or digital), Excel/CSV exports from any bank, MT940 / CAMT.053 SWIFT formats, OFX / QFX (Quicken) formats, and bank-specific XML exports. For scanned paper statements we use multi-pass OCR with table detection — achieving 99%+ accuracy on standard A4 bank statement layouts from any country.

Does it work for banks in India, UK, USA, and UAE?

Yes — template-free. We've processed statements from SBI, HDFC, ICICI, Axis, Kotak (India), Barclays, HSBC, Lloyds, NatWest (UK), Chase, Bank of America, Wells Fargo (USA), Emirates NBD, Mashreq, FAB (UAE), DBS/OCBC (Singapore), and many more. No templates needed — the AI adapts to each bank's layout.

What use cases does AI bank statement analysis support?

Credit underwriting (income verification, EMI detection, cash flow), KYC/AML (transaction pattern screening, large cash detection, round-tripping), bookkeeping (auto-categorisation, accounting software sync), due diligence (financial health snapshot for M&A), and bank reconciliation (transaction-to-invoice matching).

Is the bank statement data stored or used for training?

No. Processing is stateless — data is processed in memory and not stored unless you explicitly enable storage for your account. We never use customer data to train shared models. On-premise deployment is available for organisations that require data never to leave their own infrastructure.

AI Bank StatementAnalysis &Data Extraction