> ## Documentation Index > Fetch the complete documentation index at: https://docs.anyformat.ai/llms.txt > Use this file to discover all available pages before exploring further. # Bank statement processing > Extract the statement header and every transaction as a row, run many statements through one workflow, and get the data out **Nodes used:** [Parse](/guides/nodes/parse), [Extract](/guides/nodes/extract) This workflow reads a bank statement and returns the header (account, period, balances) as scalar fields and the transactions as rows of an `object` field. Create it once, then run every statement through it. ## The graph ```json Graph theme={null} { "name": "Bank statement processor", "nodes": [ { "id": "parse_1", "type": "parse" }, { "id": "extract_1", "type": "extract", "extraction_schema": { "fields": [ { "name": "account_holder", "description": "Name of the account holder as shown on the statement", "data_type": "string" }, { "name": "account_number", "description": "Bank account number, may be partially masked", "data_type": "string" }, { "name": "statement_period_start", "description": "First day of the statement period", "data_type": "date" }, { "name": "statement_period_end", "description": "Last day of the statement period", "data_type": "date" }, { "name": "opening_balance", "description": "Account balance at the start of the period", "data_type": "float" }, { "name": "closing_balance", "description": "Account balance at the end of the period", "data_type": "float" }, { "name": "transactions", "description": "Every transaction listed on the statement, one row each", "data_type": "object", "nested_fields": [ { "name": "date", "description": "Date of the transaction", "data_type": "date" }, { "name": "description", "description": "Transaction description or memo", "data_type": "string" }, { "name": "amount", "description": "Transaction amount: positive for deposits, negative for withdrawals", "data_type": "float" }, { "name": "type", "description": "Type of transaction", "data_type": "enum", "enum_options": [ { "name": "deposit", "description": "Incoming deposit" }, { "name": "withdrawal", "description": "Outgoing withdrawal" }, { "name": "fee", "description": "Bank fee or charge" }, { "name": "transfer", "description": "Transfer between accounts" }, { "name": "interest", "description": "Interest earned or charged" } ] } ] } ] } } ], "edges": [{ "source": "parse_1", "target": "extract_1" }] } ``` ```python Python theme={null} import os from anyformat.sdk import Client from anyformat.workflow import Schema client = Client(api_key=os.environ["ANYFORMAT_API_KEY"]) workflow = ( client.workflow("Bank statement processor") .parse() .extract([ Schema.string("account_holder", "Name of the account holder as shown on the statement"), Schema.string("account_number", "Bank account number, may be partially masked"), Schema.date("statement_period_start", "First day of the statement period"), Schema.date("statement_period_end", "Last day of the statement period"), Schema.float("opening_balance", "Account balance at the start of the period"), Schema.float("closing_balance", "Account balance at the end of the period"), Schema.object("transactions", "Every transaction listed on the statement, one row each", fields=[ Schema.date("date", "Date of the transaction"), Schema.string("description", "Transaction description or memo"), Schema.float("amount", "Transaction amount: positive for deposits, negative for withdrawals"), Schema.enum("type", "Type of transaction", options=[ Schema.option("deposit", "Incoming deposit"), Schema.option("withdrawal", "Outgoing withdrawal"), Schema.option("fee", "Bank fee or charge"), Schema.option("transfer", "Transfer between accounts"), Schema.option("interest", "Interest earned or charged"), ]), ]), ]) .create() ) ``` ```typescript TypeScript theme={null} import { Anyformat, Schema } from "@anyformat/sdk"; const af = new Anyformat({ apiKey: process.env.ANYFORMAT_API_KEY! }); const workflow = await af .workflow("Bank statement processor") .parse() .extract([ Schema.string("account_holder", "Name of the account holder as shown on the statement"), Schema.string("account_number", "Bank account number, may be partially masked"), Schema.date("statement_period_start", "First day of the statement period"), Schema.date("statement_period_end", "Last day of the statement period"), Schema.float("opening_balance", "Account balance at the start of the period"), Schema.float("closing_balance", "Account balance at the end of the period"), Schema.object("transactions", "Every transaction listed on the statement, one row each", [ Schema.date("date", "Date of the transaction"), Schema.string("description", "Transaction description or memo"), Schema.float("amount", "Transaction amount: positive for deposits, negative for withdrawals"), Schema.enum("type", "Type of transaction", [ Schema.option("deposit", "Incoming deposit"), Schema.option("withdrawal", "Outgoing withdrawal"), Schema.option("fee", "Bank fee or charge"), Schema.option("transfer", "Transfer between accounts"), Schema.option("interest", "Interest earned or charged"), ]), ]), ]) .create(); ``` ## Run and read One statement is one run. Keep each file in its own call so each statement gets its own result; a call that carries several files makes one document packet and one run. ```bash curl theme={null} for f in statement-jan.pdf statement-feb.pdf statement-mar.xlsx; do curl -X POST "https://api.anyformat.ai/v3/workflows/$WORKFLOW_ID/upload/run/" \ -H "Authorization: Bearer $ANYFORMAT_API_KEY" \ -F "files=@$f" # -> 202 {"run_id": "...", "status": "queued"}; keep each run_id done # Read each run. Repeat until "status" is "processed". curl "https://api.anyformat.ai/v3/runs/$RUN_ID/" \ -H "Authorization: Bearer $ANYFORMAT_API_KEY" ``` ```python Python theme={null} for path in ["statement-jan.pdf", "statement-feb.pdf", "statement-mar.xlsx"]: result = workflow.run(path).wait() print(result.field("account_holder").value, result.field("closing_balance").value) # An object field is a list of rows; each row maps a column name to a field for row in result.fields["transactions"]: if row["type"].value == "fee": print("fee:", row["description"].value, float(row["amount"].value)) ``` ```typescript TypeScript theme={null} import { readFile } from "node:fs/promises"; import type { ExtractedField, ExtractedRows } from "@anyformat/sdk"; for (const path of ["statement-jan.pdf", "statement-feb.pdf", "statement-mar.xlsx"]) { const file = new File([await readFile(path)], path); const run = await workflow.run(file); const result = await run.wait(); // field() returns a scalar or rows; narrow it to what the schema declares const scalar = (name: string) => result.field(name) as ExtractedField | undefined; console.log(scalar("account_holder")?.value, scalar("closing_balance")?.value); // An object field is an array of rows; each row maps a column name to a field const rows = result.field("transactions") as ExtractedRows; for (const row of rows) { if (row.type?.value === "fee") console.log("fee:", row.description?.value, Number(row.amount?.value)); } } ``` ## What comes back One statement with six transactions, trimmed to two rows. The run took 21 seconds. Every field also carries `verification_status`, `value_override` and `evidence`; only the first shows them here. ```json theme={null} { "id": "06a8d903-b9f8-7914-8000-dbc02769b602", "status": "processed", "results": { "document_packet_id": "06a8d903-b815-7b92-8000-82f7269755f2", "verification_url": "https://app.anyformat.ai/workflows/.../files/...", "parse": { "markdown": "..." }, "classifications": [], "splits": [], "extractions": [ { "split_name": null, "partition": null, "fields": { "account_holder": { "value": "Jordan Reyes", "confidence": 99.0, "verification_status": "not_verified", "value_override": null, "evidence": [{ "text": "Account holder: Jordan Reyes", "page_number": 1 }] }, "account_number": { "value": "****4471", "confidence": 99.0 }, "statement_period_start": { "value": "2026-03-01", "confidence": 99.0 }, "statement_period_end": { "value": "2026-03-31", "confidence": 99.0 }, "opening_balance": { "value": "2450.1", "confidence": 99.0 }, "closing_balance": { "value": "2983.35", "confidence": 99.0 }, "transactions": [ { "date": { "value": "2026-03-02", "confidence": 99.0, "evidence": [{ "text": "2026-03-02 Salary - Brightline Ltd +3,200.00", "page_number": 1 }] }, "description": { "value": "Salary - Brightline Ltd", "confidence": 99.0 }, "amount": { "value": "3200.0", "confidence": 99.0 }, "type": { "value": "deposit", "confidence": 95.0 } }, { "date": { "value": "2026-03-04", "confidence": 99.0 }, "description": { "value": "Rent - Harbor Apartments", "confidence": 99.0 }, "amount": { "value": "-1650.0", "confidence": 99.0 }, "type": { "value": "withdrawal", "confidence": 99.0 } } ] } } ], "edits": [] } } ``` Read the fields from `extractions[0].fields`. A scalar field is one object with `value`, `confidence` and `evidence`. An `object` field is an array of rows, and every cell in a row is that same object. Every value is a string on the wire: numbers as `"2983.35"`, dates as `"2026-03-31"`, booleans as `"True"` or `"False"`. The SDKs keep them as strings too, so convert before you add them up. A field the model could not find comes back as `{"value": null, "confidence": null, "evidence": []}`. ## Getting the data out * **In the app**, open the workflow and export the results as CSV, Excel or JSON. Each statement is a row and `transactions` expands into its own sheet or nested array. See [Outputs](/concepts/outputs). * **Over the API**, read each run as shown above, or list a workflow's runs for a date range and flatten them yourself. [Daily CSV export](/examples/daily-csv-export) is a complete script for that. ## Tips * Describe `amount` as "positive for deposits, negative for withdrawals". The sign convention then holds across banks. * An XLSX or CSV statement gives better rows than a scanned PDF. The cells are already structured and no OCR runs. * Ask for the totals the statement prints (`total_deposits`, `total_withdrawals`) as extra fields and compare them with the sum of the rows. A mismatch is the cheapest signal that a row was missed. * Statements with dense multi-page tables are a good fit for the Agentic parse tier: set `"mode": "agentic"` on the Parse node. See [Agentic parse to markdown](/examples/agentic-parse-to-markdown). * Upload and run is limited to 60 requests per minute. Submit statements serially with a short pause, or spread them across workers. ## Next steps Object fields, modes and smart lookup CSV, Excel and JSON exports from the app Flatten a day of runs into one CSV over the API The model behind the envelope