Bank Statements

Bank statements are deceptively difficult to automate. Even when the document is “digital,” banks vary layouts, terminology, and formatting by region and account type. When statements arrive as PDFs (often scans), critical fields are embedded in tables with inconsistent columns, wrapped text, and multi-page continuations.

Enterflow extracts structured transaction data from bank statements and delivers it in formats finance and accounting systems can consume—while applying validation logic that reduces reconciliation errors and highlights anomalies early.

What we typically extract from bank statements

Statement header and account details

• Bank name and branch (where present)

• Account holder name (if included)

• Account identifiers (masked account numbers/IBAN, statement reference)

• Statement period (start/end dates)

• Opening and closing balances

• Currency

Why it matters: Header information anchors the statement, enables audit trails, and prevents mis-association with the wrong account or period.

Transaction table (the core value)

For each transaction, we typically extract:

• Booking date and value date (if present)

• Debit/credit amounts and currency

• Running balance (where included)

• Counterparty / merchant name

• Reference / narrative text (often the hardest field)

• Transaction type or channel (transfer, card, fee, interest, etc., when available)

Why it matters: This is the dataset used for reconciliation, cash reporting, and exception detection.

Fees, interest, and summary lines

• Bank fees and service charges

• Interest lines

• Totals by category (if the bank provides them)

Why it matters: These items are frequent sources of reconciliation drift and can be tracked systematically once structured.

Our approach: reliable structure from inconsistent layouts

In our experience, strong bank statement automation requires more than reading text:

1) Layout and table understanding

• Identify transaction tables even when columns shift or wrap

• Handle multi-page tables and repeated headers

• Normalize inconsistent columns (some banks omit value date, others omit balance)

• Preserve row integrity when descriptions wrap across lines

2) Normalization for reconciliation

• Standardize date formats and decimal separators

• Normalize transaction signs (debit/credit) into a consistent schema

• Clean and structure narrative text (where possible)

• Tag transactions into categories using configurable rules (optional)

3) Accounting-grade validation

We implement checks to catch common issues early:

• opening/closing balance consistency checks

• running balance progression checks (when running balances exist)

• totals reconciliation (sum of debits/credits vs balance delta, where applicable)

• duplicate statement detection (same period/account)

• page-level completeness checks for scanned/partial files

This reduces “silent errors” that create downstream reconciliation gaps.

Common bank statement edge cases we design for

Bank statements often contain complications such as:

• Scanned statements with varying quality, skew, and faint text

• Multi-currency accounts and mixed-currency transactions

• Merged transaction rows or wrapped references that split across lines

• “Value date” vs “booking date” ambiguity depending on bank format

• Footnotes and legal disclaimers that resemble transaction lines

• Monthly statements with supplemental pages (fees, FX, interest)

• Masked identifiers and inconsistent account labeling across pages

We design extraction to be resilient to these patterns, and we flag low-confidence items for review where needed.

Outputs you can use immediately

We deliver structured outputs suitable for reconciliation and reporting:

• Normalized JSON with header data + transactions array

• Optional CSV export for import into accounting tools

• Optional confidence signals and validation outcomes

• Optional enrichment: transaction categorization rules, counterparty normalization, and reference parsing (project/payment IDs)

Key data we track (so accuracy is measurable)

For bank statement workflows, we typically report:

• Transaction row accuracy (row integrity and field completeness)

• Extraction completeness (did we capture all pages/rows?)

• Exception rate (rows needing review) and root causes

• Balance reconciliation success rate (header ↔ transactions consistency)

• Processing time per statement

• Cost per statement vs manual handling

Integration targets (where the data goes)

Bank statement extraction is most valuable when it feeds:

• reconciliation platforms and close checklists

• ERP/accounting systems

• treasury and cash reporting

• fraud/anomaly review workflows

• data warehouses and BI dashboards

We integrate via API, webhooks/events, secure file exchange, or adapted patterns for legacy environments.

Ready to automate bank statements reliably?

We can quickly outline:

• expected extraction coverage and accuracy,

• validation rules to ensure reconciliation integrity,

• exception handling approach,

• and a pilot-to-production rollout plan.

Contact: info@enterflow.ai
Website: https://enterflow.ai/