Clear Receipts Automatically Execution Report

Receivables◆ Seeded · Receipts

Reports the auto-clearing process, listing receipts cleared by remittance bank account, receipt number, and customer.

Sample build of the Clear Receipts Automatically Execution 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

1,940

Receipts cleared

$15.20M

Cleared value

Not cleared

Bank Account	Receipt	Customer	Cleared Date	Amount
1000-2100-000	Sample	Acme Industrial	2026-04-30	$1,240,500.00
1000-5400-000	—	Northwind Trading	2026-03-31	$842,150.75
1000-1410-000	Sample	Globex Holdings	2026-02-28	$96,400.00
2000-2100-000	—	Initech LLC	2026-01-31	$1,005,233.10
1000-6300-000	Sample	Umbrella Corp	2025-12-31	$58,720.40
1000-2100-000	Sample	Acme Industrial	2026-04-30	$1,240,500.00

AI Analyst · active

reading

The report reports the auto-clearing process by bank account and receipt.

flag

60 receipts weren't auto-cleared — they fall to manual clearing, which lags the daily cash position.

root cause & next step

Tune the clearing rules and tolerances; receipts that won't auto-clear are where the cash position falls behind reality.

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
CE_BANK_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_CASH_RECEIPTS_ALL	25	9
CE_BANK_ACCOUNTS	9	12

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 Clearing results are usually reconciled against the bank statement in one view rather than read as a separate log. Irvine rebuilds these on your data.