Netting Settlement Letter

Payables◆ Seeded · Netting

Prints a letter detailing the Receivables and Payables transactions in a netting settlement.

Sample build of the Netting Settlement Letter — 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

Letters

$4.20M

Net settled

Disputed

Trading Partner	Ap Total	Ar Total	Net Amount	Direction
Sample	$1,240,500.00	$1,240,500.00	$1,240,500.00	Sample
—	$842,150.75	$842,150.75	$842,150.75	—
Sample	$96,400.00	$96,400.00	$96,400.00	Sample
—	$1,005,233.10	$1,005,233.10	$1,005,233.10	—
Sample	$58,720.40	$58,720.40	$58,720.40	Sample
Sample	$1,240,500.00	$1,240,500.00	$1,240,500.00	Sample

AI Analyst · active

reading

The report prints the letter detailing the Receivables and Payables transactions in a netting settlement.

flag

Four settlements are disputed by the partner — disagreement over which transactions were included in the net.

root cause & next step

Agree the transaction list with the partner before settling; a disputed netting letter stalls the settlement and the residual cash.

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
POZ_SUPPLIERS	dimension	dimension
Ap Total	measure	measure
Ar Total	measure	measure
Net 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.

Payables 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
AP_INVOICES_ALL	63	15
RA_CUSTOMER_TRX_ALL	58	16
POZ_SUPPLIERS	145	75

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 The letter layout typically needs counterparty-specific and language customization. Irvine rebuilds these on your data.