Uploading files

This guide covers how to import transaction data from external sources into Matcher for reconciliation.

Supported formats

Matcher accepts transaction files in three formats:

CSV: Comma-separated values with headers. Most common for bank exports.
JSON: Array of transaction objects. Best for API integrations.
XML: Structured elements. Common for enterprise systems.

File structure requirements

Each file must contain transaction records with fields that can be mapped to Matcher’s internal schema.

Required fields

Every transaction must have these fields (or mappable equivalents):

Field	Type	Description
`transaction_id`	String	Unique identifier within the source
`amount`	Decimal	Transaction amount (positive or negative)
`currency`	String	ISO 4217 currency code
`date`	Date/DateTime	Transaction date

Optional fields

Field	Type	Description
`reference`	String	External reference or description
`counterparty`	String	Other party in the transaction
`type`	String	Transaction type (credit, debit, etc.)
`metadata`	Object	Additional custom fields

Format examples

CSV

CSV Requirements:

First row must be column headers
UTF-8 encoding
Comma delimiter (configurable)
Quote fields containing commas or newlines

Code example

 transaction_id,amount,currency,date,reference,type
 BANK-2024-001,1500.00,USD,2024-01-15,Invoice #1234,credit
 BANK-2024-002,-250.00,USD,2024-01-15,Service fee,debit
 BANK-2024-003,3200.50,USD,2024-01-16,Customer payment,credit
 BANK-2024-004,-89.99,USD,2024-01-16,Subscription,debit

JSON

JSON Requirements:

Root element must be an array
Consistent field names across objects
UTF-8 encoding

Code example

[
  {
    "transaction_id": "BANK-2024-001",
    "amount": 1500.0,
    "currency": "USD",
    "date": "2024-01-15",
    "reference": "Invoice #1234",
    "type": "credit"
  },
  {
    "transaction_id": "BANK-2024-002",
    "amount": -250.0,
    "currency": "USD",
    "date": "2024-01-15",
    "reference": "Service fee",
    "type": "debit"
  }
]

XML

XML Requirements:

Valid XML with declaration
Root element containing transaction elements
UTF-8 encoding

Code example

  <?xml version="1.0" encoding="UTF-8"?>
  <transactions>
    <transaction>
      <transaction_id>BANK-2024-001</transaction_id>
      <amount>1500.00</amount>
      <currency>USD</currency>
      <date>2024-01-15</date>
     <reference>Invoice #1234</reference>
      <type>credit</type>
    </transaction>
    <transaction>
      <transaction_id>BANK-2024-002</transaction_id>
      <amount>-250.00</amount>
      <currency>USD</currency>
      <date>2024-01-15</date>
      <reference>Service fee</reference>
      <type>debit</type>
      </transaction>
  </transactions>

Upload via API

Use the import endpoint to upload transaction files.

Preview before uploading

Before committing a file for ingestion, you can preview it to verify column detection and sample data. This helps catch field mapping issues early.

cURL

curl -X POST "https://api.matcher.example.com/v1/imports/contexts/{contextId}/sources/{sourceId}/preview" \
 -H "Authorization: Bearer $TOKEN" \
 -F "file=@bank_statement_january.csv" \
 -F "max_rows=5"

Response

{
  "columns": ["transaction_id", "amount", "currency", "date", "reference"],
  "sampleRows": [
    ["BANK-2024-001", "1500.00", "USD", "2024-01-15", "Invoice #1234"],
    ["BANK-2024-002", "-250.00", "USD", "2024-01-15", "Service fee"]
  ],
  "rowCount": 2,
  "format": "csv"
}

API Reference: Preview file

Single file upload

cURL

curl -X POST "https://api.matcher.example.com/v1/imports/contexts/{contextId}/sources/{sourceId}/upload" \
 -H "Authorization: Bearer $TOKEN" \
 -F "format=csv" \
 -F "file=@bank_statement_january.csv"

Send the format field before the file part. If file arrives first, the format is inferred from the filename extension (.csv/.json/.xml). Upload returns 202 Accepted with the created job.

API Reference: Upload file

Response

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "contextId": "969a11cd-6b7d-4e71-b82b-0828e0603149",
  "sourceId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "QUEUED",
  "fileName": "bank_statement_january.csv",
  "totalRows": 0,
  "persistedRows": 0,
  "droppedDuplicateRows": 0,
  "failedRows": 0,
  "failureRatePercent": 0,
  "completedWithErrors": false,
  "createdAt": "2024-01-20T10:30:00Z"
}

Check import status

cURL

curl -X GET https://api.matcher.example.com/v1/imports/contexts/{contextId}/jobs/{jobId} \
 -H "Authorization: Bearer $TOKEN"

API Reference: Get import status

Response (Processing)

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "contextId": "969a11cd-6b7d-4e71-b82b-0828e0603149",
  "sourceId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "PROCESSING",
  "fileName": "bank_statement_january.csv",
  "totalRows": 1250,
  "startedAt": "2024-01-20T10:30:05Z"
}

Response (Completed)

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "contextId": "969a11cd-6b7d-4e71-b82b-0828e0603149",
  "sourceId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "status": "COMPLETED",
  "completedWithErrors": true,
  "fileName": "bank_statement_january.csv",
  "totalRows": 1250,
  "persistedRows": 1233,
  "droppedDuplicateRows": 12,
  "failedRows": 5,
  "failureRatePercent": 1,
  "reviewRows": 0,
  "diagnosis": "",
  "startedAt": "2024-01-20T10:30:05Z",
  "completedAt": "2024-01-20T10:30:45Z"
}

Per-row parse/normalization errors are not embedded in the job. When completedWithErrors is true (or the job FAILED), fetch the details from GET /v1/imports/contexts/{contextId}/jobs/{jobId}/errors (capped at 100 stored rows, with totalErrors/truncated accounting). For a wholesale FAILED job, diagnosis carries a safe one-line reason.

Import job status values

Status	Description
`QUEUED`	Job received, awaiting a worker
`PROCESSING`	File is being parsed and normalized
`COMPLETED`	Import finished (check `completedWithErrors` for partial failures)
`FAILED`	Import aborted wholesale (see `diagnosis`)

Validation and error handling

Matcher validates uploaded files at multiple stages.

Validation stages

Format Validation

Verifies the file is valid CSV, JSON, or XML with correct structure.

Schema Validation

Checks that required fields are present and match the configured field map.

Data Type Validation

Validates amounts are valid decimals, dates are parseable, currencies are valid ISO codes.

Business Rule Validation

Applies context-specific rules like date ranges, amount limits, etc.

Common validation errors

Error	Cause	Solution
`INVALID_FORMAT`	File cannot be parsed	Check file encoding and structure
`MISSING_REQUIRED_FIELD`	Required field not found	Verify field mapping configuration
`INVALID_AMOUNT`	Amount not a valid number	Check for currency symbols or commas in numbers
`INVALID_DATE`	Date cannot be parsed	Use ISO 8601 format (YYYY-MM-DD)
`UNKNOWN_CURRENCY`	Currency code not recognized	Use ISO 4217 codes (USD, EUR, BRL)
`DATE_OUT_OF_RANGE`	Date before/after allowed range	Check context date boundaries

Handling errors

By default, valid rows are imported even if some rows have errors. Configure error handling behavior through context settings or handle errors after import completion by reviewing the job status response.

Duplicate detection

Matcher automatically detects and handles duplicate transactions to prevent double-counting.

How duplicates are detected

Duplicates are identified by the row’s dedup key within a source:

source_id
external_id (the source-system transaction identifier)

If a row repeats that key—within the same upload or against already-persisted data—it is treated as a duplicate.

Duplicate handling options

Set the duplicate_policy key in the source config to control handling:

Policy	Behavior
`KEEP_FIRST` (default)	Keeps the first occurrence and silently drops repeats, counted in `droppedDuplicateRows`
`REJECT`	Turns each repeated row into an ingestion row error, counted in `failedRows`
`FLAG_AS_EXCEPTION`	Drops the repeat and raises a `DUPLICATE_TRANSACTION` exception on the surviving transaction (subset reported as `flaggedDuplicateRows`)

When the key is absent, KEEP_FIRST applies.

Viewing duplicate details

The import summary shows how many duplicates were found:

{
  "totalRows": 1000,
  "persistedRows": 950,
  "droppedDuplicateRows": 50,
  "failedRows": 0,
  "failureRatePercent": 0
}

Batch uploads

For large reconciliation jobs, you can upload multiple files in sequence.

Upload multiple files

# Upload bank statement
curl -X POST "https://api.matcher.example.com/v1/imports/contexts/{contextId}/sources/{bankSourceId}/upload" \
 -H "Authorization: Bearer $TOKEN" \
 -F "file=@bank_january.csv" \
 -F "format=csv"

# Upload ledger export
curl -X POST "https://api.matcher.example.com/v1/imports/contexts/{contextId}/sources/{ledgerSourceId}/upload" \
 -H "Authorization: Bearer $TOKEN" \
 -F "file=@ledger_january.csv" \
 -F "format=csv"

Wait for all imports

Before running matching, ensure all imports are complete:

# List imports for context
curl -X GET "https://api.matcher.example.com/v1/imports/contexts/{contextId}/jobs" \
 -H "Authorization: Bearer $TOKEN"

Search uploaded transactions

After importing files, you can search across all transactions in a context to verify data quality or investigate specific records.

cURL

curl -X GET "https://api.matcher.example.com/v1/imports/contexts/{contextId}/transactions/search?q=Invoice&amount_min=1000&status=UNMATCHED" \
 -H "Authorization: Bearer $TOKEN"

Response

{
  "items": [
    {
      "id": "019c96a0-2a10-7dfe-b5c1-8a1b2c3d4e5f",
      "sourceId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
      "amount": "1500.00",
      "currency": "USD",
      "date": "2024-01-15T00:00:00Z",
      "description": "Invoice #1234",
      "status": "UNMATCHED"
    }
  ],
  "total": 1,
  "limit": 20,
  "offset": 0
}

API Reference: Search transactions

Supported filters include amount_min, amount_max, date_from, date_to, currency, source_id, status, and free-text search via the q parameter.

Best practices

Validate files before upload

Check file format and encoding locally before uploading. This catches obvious errors faster.

# Check CSV is valid
head -5 transactions.csv

# Check encoding
file transactions.csv

Use consistent date formats

Standardize on ISO 8601 format (YYYY-MM-DD or YYYY-MM-DDTHH:MM:SSZ) across all sources to avoid parsing issues.

Include transaction IDs

Always include unique transaction IDs from the source system. This enables proper duplicate detection and audit trails.

Handle negative amounts consistently

Decide on a convention (negative for debits, positive for credits) and apply it consistently. Document this in your field mapping.

Upload incrementally for large files

For very large files (>50MB), consider splitting into smaller chunks by date range. This improves reliability and allows partial retries.

Set up automated uploads

For recurring reconciliation, automate file uploads using scheduled jobs or webhooks from source systems.

# Example: Daily upload via cron
0 6 * * * /scripts/upload_bank_statement.sh

Overview

Guides

Matcher UI

MCP Server

Operations

Uploading files

Supported formats

File structure requirements

Required fields

Optional fields

Format examples

CSV

JSON

XML

Upload via API

Preview before uploading

Response

Single file upload

Response

Check import status

Response (Processing)

Response (Completed)

Import job status values

Validation and error handling

Validation stages

Common validation errors

Handling errors

Duplicate detection

How duplicates are detected

Duplicate handling options

Viewing duplicate details

Batch uploads

Upload multiple files

Wait for all imports

Search uploaded transactions

Response

Best practices

Next steps

Reviewing Matches

Field Mapping

​Supported formats

​File structure requirements

​Required fields

​Optional fields

​Format examples

​CSV

​JSON

​XML

​Upload via API

​Preview before uploading

​Response

​Single file upload

​Response

​Check import status

​Response (Processing)

​Response (Completed)

​Import job status values

​Validation and error handling

​Validation stages

​Common validation errors

​Handling errors

​Duplicate detection

​How duplicates are detected

​Duplicate handling options

​Viewing duplicate details

​Batch uploads

​Upload multiple files

​Wait for all imports

​Search uploaded transactions

​Response

​Best practices

​Next steps

Reviewing Matches

Field Mapping

Supported formats

File structure requirements

Required fields

Optional fields

Format examples

CSV

JSON

XML

Upload via API

Preview before uploading

Response

Single file upload

Response

Check import status

Response (Processing)

Response (Completed)

Import job status values

Validation and error handling

Validation stages

Common validation errors

Handling errors

Duplicate detection

How duplicates are detected

Duplicate handling options

Viewing duplicate details

Batch uploads

Upload multiple files

Wait for all imports

Search uploaded transactions

Response

Best practices

Next steps