SWIFT MT300 Foreign Exchange Confirmation

This dataset provides comprehensive records of foreign exchange spot, forward, and swap transactions confirmed via SWIFT MT300 messages. Each record includes transaction details, currency legs, rates, involved parties, and settlement instructions, making it ideal for regulatory reporting, reconciliation, and risk management in financial institutions.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Automated reconciliation of FX trades between counterparties

Sample rows

preview · 8 of 500 rows · all 19 columns
transaction_idstringconfirmation_statusstringamount_currency_1floatcurrency_1stringtrade_datedatevalue_datedatetransaction_typestringcurrency_2stringamount_currency_2floatexchange_ratefloatparty_1_namestringparty_1_bicstringparty_2_namestringparty_2_bicstringconfirmation_datetimedatetimereference_numberstringremarksstringsettlement_instructions_currency_1stringsettlement_instructions_currency_2string
FXT-USD-EUR-001confirmed350000USD2024-06-112024-06-12spotEUR3150000.9Citibank NACITIUS33Deutsche Bank AGDEUTDEFF2024-06-11T14:12:02USD-EUR-20240611-01blankCitibank NA, Acc# 12345678, New YorkDeutsche Bank AG, Acc# 87654321, Frankfurt
FXT-USD-JPY-002confirmed250000USD2024-06-072024-06-09spotJPY36750000147JPMorgan ChaseCHASUS33Mizuho Bank LtdMHCBJPJT2024-06-07T09:45:40USD-JPY-20240607-02blankJPMorgan Chase, Acc# 23456789, New YorkMizuho Bank Ltd, Acc# 98765432, Tokyo
FXT-GBP-USD-003confirmed200000GBP2024-06-102024-06-11spotUSD2540001.27Barclays Bank PLCBARCGB22Bank of AmericaBOFAUS3N2024-06-10T16:00:22GBP-USD-20240610-03blankBarclays Bank PLC, Acc# 34567890, LondonBank of America, Acc# 87654321, Charlotte
FXT-CAD-EUR-004confirmed100000CAD2024-06-062024-06-07spotEUR675000.675Royal Bank of CanadaROYCCAT2BNP ParibasBNPAFRPP2024-06-06T13:20:10CAD-EUR-20240606-04blankRoyal Bank of Canada, Acc# 45678901, TorontoBNP Paribas, Acc# 12349876, Paris
FXT-USD-EUR-005confirmed800000USD2024-06-082024-06-09spotEUR7200000.9Wells Fargo BankWFBIUS6SING Bank NVINGBNL2A2024-06-08T15:33:45USD-EUR-20240608-05blankWells Fargo Bank, Acc# 56789012, San FranciscoING Bank NV, Acc# 21098765, Amsterdam
FXT-USD-JPY-006confirmed500000USD2024-06-052024-06-06spotJPY74000000148Goldman SachsGSCMUS33Sumitomo MitsuiSMBCJPJT2024-06-05T11:10:30USD-JPY-20240605-06blankGoldman Sachs, Acc# 67890123, New YorkSumitomo Mitsui, Acc# 32109876, Osaka
FXT-USD-EUR-007confirmed1500000USD2024-06-092024-06-10spotEUR13500000.9HSBC Bank PLCHBUKGB4BUniCredit SpAUNCRITMM2024-06-09T17:00:18USD-EUR-20240609-07blankHSBC Bank PLC, Acc# 78901234, LondonUniCredit SpA, Acc# 43210987, Milan
FXT-GBP-USD-008confirmed600000GBP2024-06-102024-06-12spotUSD7620001.27Lloyds Bank PLCLOYDGB2LMorgan StanleyMSNYUS332024-06-10T15:11:06GBP-USD-20240610-08blankLloyds Bank PLC, Acc# 89012345, LondonMorgan Stanley, Acc# 54321098, New York

What the 500 rows show

from the 500-row sample

Cancelled (confirmation status) stands out: mean amount_currency_1 is 107,819,027, against 1,822,013 for the rest.

  • 350,000median amount_currency_1
  • 3transaction types
  • 14currency 2s
  • 122party 2 bics
  • 993,750median amount_currency_2
  • 1.3median exchange_rate
Mean amount_currency_1 by confirmation_status500 rows
0100M200M4.7Mpending124 rows659.3kconfirmed310 rows107.8Mcancelled66 rows
amount_currency_1500 rows, in bands of 100M
02505004920000101150500M1Bamount_currency_1 →

Median 350,000, from 0.00 to 1,000,000,000.

currency_1500 rows · top 10 of 15 values
  1. USD129
  2. GBP65
  3. EUR50
  4. AUD32
  5. CAD31
  6. SGD31
  7. DKK31
  8. INR29
  9. NZD27
  10. SEK22
19 columns by typefrom the column list below
  • string 13
  • float 3
  • date 2
  • datetime 1

Columns

19 columns in three groups
blueprint · 19 columns
columntypedescriptionexample
Text 13 columns
transaction_idstringUnique identifier for the FX transaction confirmationuniqueFXT-USD-EUR-001
transaction_typestringType of FX transaction (spot, forward, swap)spot · forward · swapspot
currency_1stringISO 4217 code for the first currency legUSD
currency_2stringISO 4217 code for the second currency legEUR
party_1_namestringName of the first party involved in the transactionCitibank NA
party_1_bicstringSWIFT/BIC code of the first partyCITIUS33
party_2_namestringName of the second party involved in the transactionDeutsche Bank AG
party_2_bicstringSWIFT/BIC code of the second partyDEUTDEFF
settlement_instructions_currency_1stringSettlement instructions for the first currency legNAB, Acc# 2017712, Perth
settlement_instructions_currency_2stringSettlement instructions for the second currency legDeutsche Bank AG, Acc# 87…
confirmation_statusstringStatus of the transaction confirmationpending · confirmed · cancelledconfirmed
reference_numberstringReference number provided by one or both partiesoptionalUSD-EUR-20240611-01
remarksstringAdditional remarks or notes regarding the transactionoptionalSwap - boundary leg
Numbers 3 columns
amount_currency_1floatAmount in the first currency leg0 or more350000
amount_currency_2floatAmount in the second currency leg0 or more315000
exchange_ratefloatAgreed FX rate between currency_1 and currency_20 or more0.9
Dates and times 3 columns
trade_datedateDate on which the FX transaction was agreed2024-06-11
value_datedateDate on which the FX transaction is settled2024-06-12
confirmation_datetimedatetimeTimestamp when the confirmation was processed2024-06-11T14:12:02

Use it for

  • median amount …350,000500 rowsmean amount currency …4.7Mpending659.3kconfir…107.8Mcancel…

    A finance dashboard

    Amount_currency_1 by confirmation_status and a breakdown of currency_1. Excel, Power BI or Tableau.

  • Why do the 66 cancelled rows have a mean amount_currency_1 of 107,819,027?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable transactions with trade_date, value_date and transaction_type to fill a screen in front of a buyer.

Not quite right?

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This dataset500 rows19 columns
Yours1,000 rows19 columns

blueprint · swift-mt300-foreign-exchange-confirmation

Behind this dataset

Same schema. As many rows as you need.

These 500 rows came out of a blueprint — 19 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Unique transaction reference (field 20)
  • Common reference agreed by both parties (field 22A)
  • Trade date in YYYYMMDD format
  • Currency pair specified (e.g., EUR/USD)
  • Exchange rate to 5+ decimal places
  • Party A buys/sells amounts
  • Party B buys/sells amounts
  • Value date for settlement (field 30)
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
swift-mt300-foreign-exchange-confirmation

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