Billing History Report

Receivables◆ Seeded · Billing

Summarizes each customer site's transactions and the activity against them over a date range — original amount, current balance due, total payments applied, credit-memo amounts, and adjustment amounts.

Sample build of the Billing History 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

680

Customer sites

$48.00M

Billed

$9.40M

Current balance

Customer Site	Transaction	Original	Activity	Current Balance
Acme Industrial	Sample	Sample	Sample	$1,240,500.00
Northwind Trading	—	—	—	$842,150.75
Globex Holdings	Sample	Sample	Sample	$96,400.00
Initech LLC	—	—	—	$1,005,233.10
Umbrella Corp	Sample	Sample	Sample	$58,720.40
Acme Industrial	Sample	Sample	Sample	$1,240,500.00

AI Analyst · active

reading

The report summarizes each site's transactions and the receipts and adjustments applied against them.

flag

A few sites show heavy adjustment activity against billed amounts — recurring billing corrections at those sites.

root cause & next step

Trace the adjustment pattern to a pricing or contract setup issue; repeated billing adjustments mean the invoice was wrong at source.

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
AR_PAYMENT_SCHEDULES_ALL	dimension	dimension
AR_RECEIVABLE_APPLICATIONS_ALL	dimension	dimension
AR_ADJUSTMENTS_ALL	dimension	dimension
Current Balance	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
RA_CUSTOMER_TRX_ALL	58	16
AR_PAYMENT_SCHEDULES_ALL	32	6
AR_RECEIVABLE_APPLICATIONS_ALL	35	2
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 The seeded layout is per-site and static; finance usually wants a customer-level rollup with drill to each transaction and its applications. Irvine rebuilds these on your data.