Potential Reconciling Items Report

Receivables◆ Seeded · Accounting

Flags journal items that may have posted to the wrong general ledger accounts — for example adjustments routed to balance-sheet rather than income accounts — by reporting level, accounting date, and balancing segment.

Sample build of the Potential Reconciling Items 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

140

Flagged items

$220K

Misrouted value

Adjustments

Transaction	Activity	Expected Account	Posted Account	Amount
Sample	Sample	1000-2100-000	1000-2100-000	$1,240,500.00
—	—	1000-5400-000	1000-5400-000	$842,150.75
Sample	Sample	1000-1410-000	1000-1410-000	$96,400.00
—	—	2000-2100-000	2000-2100-000	$1,005,233.10
Sample	Sample	1000-6300-000	1000-6300-000	$58,720.40
Sample	Sample	1000-2100-000	1000-2100-000	$1,240,500.00

AI Analyst · active

reading

The report flags journal items that may have posted to the wrong GL accounts, such as adjustments routed to balancing accounts.

flag

$220K of adjustments routed to accounts other than the expected receivable or revenue accounts — the AR-to-GL reconciliation won't tie.

root cause & next step

Correct the receivables-activity GL setup behind the misrouted adjustments; a misrouted adjustment is the usual AR-to-GL reconciling item.

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
AR_ADJUSTMENTS_ALL	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.

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_DISTRIBUTIONS_ALL	12	6
RA_CUSTOMER_TRX_ALL	58	16
AR_ADJUSTMENTS_ALL	19	2

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 Run it after any setup change; teams typically wire the output into the close checklist so misderived accounts are caught before final accounting. Irvine rebuilds these on your data.