Receivables Aging by General Ledger Account Report

Receivables◆ Seeded · Receivables balances

Ages outstanding receivables by general ledger account as of a chosen date, showing customer, transaction number, due date, and outstanding amount across aging buckets and balancing segment — the report most teams use to reconcile AR to the GL.

Related Pairs with the flagship Receivables Aging Report — this view splits the aging by GL account for the reserve tie-out.

Sample build of the Receivables Aging by General Ledger Account 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

GL accounts

$24.00M

Open AR

$2.10M

Over 90 days

Gl Account	Customer	Due Date	Current	31-60	61-90	90+
1000-2100-000	Acme Industrial	2026-04-30	Sample	Sample	Sample	Sample
1000-5400-000	Northwind Trading	2026-03-31	—	—	—	—
1000-1410-000	Globex Holdings	2026-02-28	Sample	Sample	Sample	Sample
2000-2100-000	Initech LLC	2026-01-31	—	—	—	—
1000-6300-000	Umbrella Corp	2025-12-31	Sample	Sample	Sample	Sample
1000-2100-000	Acme Industrial	2026-04-30	Sample	Sample	Sample	Sample

AI Analyst · active

reading

The report ages open receivables by the GL account they post to, as of a date.

flag

$2.1M is over 90 days, concentrated in one receivable account — aged AR that should be reserved or written off.

root cause & next step

Reconcile the aged buckets to the GL receivable accounts and act on the 90+ tier; aging by account is how the reserve gets justified.

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_DISTRIBUTIONS_ALL	dimension	dimension
HZ_CUST_ACCOUNTS	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_PAYMENT_SCHEDULES_ALL	32	6
RA_CUSTOMER_TRX_ALL	58	16
AR_DISTRIBUTIONS_ALL	12	6
HZ_CUST_ACCOUNTS	14	43

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 Buckets are fixed at run time and there is no drill to the receipt or adjustment behind a balance; the build adds runtime buckets, full drill, and multi-business-unit consolidation. Irvine rebuilds these on your data.