Receivables Projected Gains and Losses Report

Receivables◆ Seeded · Receivables balances

Compares the revalued amount of foreign-currency transactions to the entered amount and prints the projected unrealized gain or loss by ledger currency, customer, and transaction.

Sample build of the Receivables Projected Gains and Losses Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Sample build · illustrative

Filters

Period

FEB-26

Ledger

US Primary

Currency

USD

640

FX transactions

-$160K

Projected loss

Currencies

Customer	Currency	Entered Amount	Revalued Amount	Projected G/l
Acme Industrial	USD	$1,240,500.00	$1,240,500.00	Sample
Northwind Trading	USD	$842,150.75	$842,150.75	—
Globex Holdings	USD	$96,400.00	$96,400.00	Sample
Initech LLC	USD	$1,005,233.10	$1,005,233.10	—
Umbrella Corp	USD	$58,720.40	$58,720.40	Sample
Acme Industrial	USD	$1,240,500.00	$1,240,500.00	Sample

AI Analyst · active

reading

The report compares revalued to entered amounts and projects the unrealized gain or loss.

flag

A projected -$160K loss if rates hold to settlement — FX exposure on open AR worth hedging or watching.

root cause & next step

Share the projection with treasury; projected FX losses on receivables are a cash-and-hedging decision, not just an accounting entry.

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.

The SQL data set is authored to this report's exact spec during the build and ships as the BI Publisher data model plus a matching dbt model — one definition, both layers.

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
RA_CUSTOMER_TRX_ALL	dimension	dimension
GL_DAILY_RATES	dimension	dimension
Entered Amount	measure	measure
Revalued 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.

Receivables 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
AR_PAYMENT_SCHEDULES_ALL	32	6
RA_CUSTOMER_TRX_ALL	58	16
GL_DAILY_RATES	1	10

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.

Customization note Often turned into an FX-exposure dashboard that trends unrealized gain/loss by currency rather than a point-in-time listing. Irvine rebuilds these on your data.