Bank Statement Parser Use Cases: Treasury, Reconciliation, and Compliance

Bank Statement Parser handles real-world financial workflows: MT940-to-CAMT migration for treasury teams, automated reconciliation, compliance pipelines with PII redaction, SFTP ingestion, multi-bank consolidation, and secure ZIP batch processing.

Treasury: MT940 to CAMT.053 Migration

Result: A single API call handles both MT940 and CAMT.053 during the SWIFT migration window (November 2025–November 2028), eliminating the need for separate parsing pipelines.

Treasury teams worldwide are migrating from MT940 to CAMT.053 ahead of the November 2027 SWIFT deadline. Bank Statement Parser handles both formats with a single API, making the transition seamless.

from bankstatementparser import create_parser, detect_statement_format

# Process both MT940 and CAMT.053 with the same code
for file in daily_statement_files:
    fmt = detect_statement_format(file)
    parser = create_parser(file, fmt)
    df = parser.parse()
    load_to_treasury_system(df)

Automated Reconciliation

Result: Format-agnostic DataFrames with built-in deduplication reduce manual matching effort and catch duplicate entries before they reach your ledger.

Parse bank statements and match against internal records automatically. The unified DataFrame output makes reconciliation logic format-agnostic.

from bankstatementparser import CamtParser, Deduplicator

parser = CamtParser("bank_statement.xml")
bank_txns = parser.parse()

# Deduplicate before reconciliation
dedup = Deduplicator()
result = dedup.deduplicate(dedup.from_dataframe(bank_txns))
clean_txns = result.unique_transactions

# Match against internal records
unmatched = reconcile(clean_txns, internal_ledger)

Compliance and Audit Pipelines

Result: Deterministic output and automatic PII redaction produce audit-ready logs that satisfy regulatory reproducibility requirements without additional tooling.

Build audit-ready pipelines with PII redaction and deterministic output. Every run produces identical results for the same input, satisfying regulatory reproducibility requirements.

from bankstatementparser import CamtParser

parser = CamtParser("statement.xml")

# Stream with PII redacted for audit logs
for txn in parser.parse_streaming(redact_pii=True):
    audit_log.write(txn)

# Export full data for secure internal processing
parser.export_csv("archive/statement.csv")

SFTP-to-DataFrame Workflows

Result: Parse directly from bytes with zero disk I/O, fitting natively into SFTP and API-driven bank connectivity workflows.

Many banks deliver statements via SFTP. Parse directly from bytes without writing to disk.

from bankstatementparser import CamtParser

xml_bytes = sftp_client.read("daily_statement.xml")
parser = CamtParser.from_bytes(xml_bytes, source_name="daily.xml")
df = parser.parse()

Multi-Bank Consolidation

Result: Parallel parsing across HSBC (CAMT), Barclays (MT940), Revolut (CSV), and Wise (OFX) produces a single normalised dataset in one call.

Consolidate statements from multiple banks using different formats into a single normalised dataset.

from bankstatementparser import parse_files_parallel

results = parse_files_parallel([
    "hsbc/camt053.xml",
    "barclays/mt940.sta",
    "revolut/transactions.csv",
    "wise/statement.ofx",
])

all_transactions = pd.concat([r.transactions for r in results if r.status == "success"])

Batch Processing with ZIP Archives

Result: Built-in ZIP bomb protection (100:1 ratio limit, 10 MB entry cap, encrypted entry rejection) lets you process monthly statement archives safely.

Process zipped statement archives securely with built-in ZIP bomb protection.

from bankstatementparser import iter_secure_xml_entries, CamtParser

for entry in iter_secure_xml_entries("monthly_statements.zip"):
    parser = CamtParser.from_bytes(entry.xml_bytes, source_name=entry.source_name)
    df = parser.parse()
    save_to_warehouse(entry.source_name, df)

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