GL Daily Rates

General Ledger

The daily currency conversion rates loaded in the general ledger — rate by currency pair, conversion type, and date — the reference data every revaluation, translation, and foreign-currency report depends on.

Related The rate source behind the Unrealized Foreign Currency Revaluation reports and any multi-currency translation.

Sample build of the GL Daily Rates — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

GL Daily Rates

Sample build · illustrative

Filters

Sample

Conversion Type

Standard

End Date

2026-02-28

From Currency

USD

Start Date

2026-02-28

To Currency

USD

Currency pairs

Conversion types

Missing month-end

From Currency	To Currency	Conversion Type	Rate Date	Rate
USD	USD	Standard	2026-04-30	Sample
USD	USD	Corporate	2026-03-31	—
USD	USD	Standard	2026-02-28	Sample
USD	USD	Default	2026-01-31	—
USD	USD	Standard	2025-12-31	Sample
USD	USD	Standard	2026-04-30	Sample

AI Analyst · active

reading

The report reads GL_DAILY_RATES by currency pair, conversion type, and date.

flag

Three currency pairs have no rate on month-end dates — any revaluation or translation on those dates falls back to an older rate or fails outright.

root cause & next step

Load the missing month-end rates; a missing daily rate is the single most common cause of a failed period-end revaluation.

Illustrative data. The live interactive version — drill-through, filters, export, and the AI Analyst — runs on your warehouse. See it live →

This is the report's BI Publisher data model — the SQL data set BI Publisher runs against Oracle tables to produce the output. The same SQL becomes a dbt model in your warehouse, so one definition drives both the formatted report and the analytics layer.

Data sources

How it interconnects: this data set reads the physical tables above. Those same tables surface in OTBI as subject areas and in BICC as PVOs — three lenses on one source. Open any table to trace its subject areas and View Objects.

SQL data set · genericized · parameterized · no hardcoded segments

Show / hide SQL

select	 rate."FROM_CURRENCY" as "FROM_CURRENCY",
	 rate."TO_CURRENCY" as "TO_CURRENCY",
	 TO_CHAR(rate."CONVERSION_DATE", 'FMMM/DD/YYYY') as "CONVERSION_DATE",
	 types."USER_CONVERSION_TYPE" as "USER_CONVERSION_TYPE",
	 rate."CONVERSION_RATE" as "CONVERSION_RATE",
	 TO_CHAR(rate."CREATION_DATE", 'FMMM/DD/YYYY HH24:MI:SS') as "CREATION_DATE",
	 rate."CREATED_BY" as "CREATED_BY",
	 TO_CHAR(rate."LAST_UPDATE_DATE", 'FMMM/DD/YYYY HH24:MI:SS') as "LAST_UPDATE_DATE",
	 rate."LAST_UPDATED_BY" as "LAST_UPDATED_BY" 
 from	"FUSION_RO"."GL_DAILY_CONVERSION_TYPES" types,
	"FUSION_RO"."GL_DAILY_RATES" rate
 where   types."CONVERSION_TYPE"=rate."CONVERSION_TYPE" AND
		CONVERSION_DATE BETWEEN NVL(:P_START_DATE, TRUNC(SYSDATE)) 
                        AND NVL(:P_END_DATE, TRUNC(SYSDATE)) AND
		rate.CONVERSION_TYPE IN (:P_CONVERSION_TYPE) AND
		(:P_FROM_CURRENCY IS NULL OR rate.FROM_CURRENCY = :P_FROM_CURRENCY) AND
		(:P_TO_CURRENCY IS NULL OR rate.TO_CURRENCY = :P_TO_CURRENCY)

:MI :P_CONVERSION_TYPE :P_END_DATE :P_FROM_CURRENCY :P_START_DATE :P_TO_CURRENCY :SS

The data-warehouse model — one fact surrounded by conformed dimensions (what you slice by) and measures (what you aggregate), expressed as dbt so it migrates with you. Grain: one row per source transaction.

●— fact → dimension join

Element	Type	Definition
GL_DAILY_CONVERSION_TYPES	dimension	dimension
Amount	measure	measure

Runs on your cloud warehouse — Snowflake, BigQuery, Redshift, or Synapse on AWS, Google Cloud, Azure, or any provider. Reconciled to the source control total — 0% variance by design. You own the code, the model, and the data.

How the data gets here: a BICC bulk extract of the source tables above, on the same pattern for every report. See the extraction pattern & data flow →

See the complete model

How this report's fact and dimensions fit the full picture, via conformed keys.

General Ledger data model →Enterprise model →

Every source object behind this report. Each linked table has its own page with full column descriptions, drawn from the Oracle BICC lineage and articulated for practitioners.

Table	Reporting columns	Subject areas
GL_DAILY_RATES	1	10
GL_DAILY_CONVERSION_TYPES	2	20

Reporting columns = fields the report selects that are exposed as analytics attributes; subject areas = the OTBI subject areas the table appears in. Setup and configuration tables (master data, ledger and book setup, lookups) are referenced by the report's joins but aren't exposed as analytics columns or subject areas — that's expected, not a gap.