Payables Matching Hold Detail Report

Payables◆ Seeded · Invoices

Provides payables and purchasing detail for invoices with matching holds and matching hold releases.

Sample build of the Payables Matching Hold Detail 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

180

On matching hold

$2.10M

Held value

Over 15 days

Invoice	Supplier	Hold Reason	Hold Date	Release Date	Amount
Sample	Acme Industrial	Standard	2026-04-30	2026-04-30	$1,240,500.00
—	Northwind Trading	Corporate	2026-03-31	2026-03-31	$842,150.75
Sample	Globex Holdings	Standard	2026-02-28	2026-02-28	$96,400.00
—	Initech LLC	Default	2026-01-31	2026-01-31	$1,005,233.10
Sample	Umbrella Corp	Standard	2025-12-31	2025-12-31	$58,720.40
Sample	Acme Industrial	Standard	2026-04-30	2026-04-30	$1,240,500.00

AI Analyst · active

reading

The report provides payables and purchasing detail for invoices with matching holds and releases.

flag

60 holds are over 15 days old — invoices aging on hold, risking late fees and missed discounts.

root cause & next step

Route aged matching holds to buyers for PO or receipt correction; a stale matching hold is a payment that's quietly overdue.

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
AP_INVOICES_ALL	dimension	dimension
AP_INVOICE_DISTRIBUTIONS_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.

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_HOLDS_ALL	11	1
AP_INVOICES_ALL	63	15
AP_INVOICE_DISTRIBUTIONS_ALL	59	11

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 Usually rebuilt into an aged-holds worklist routed to buyers, with days-on-hold and dollar impact. Irvine rebuilds these on your data.