Process Receipts Through Lockbox Execution Report

Receivables◆ Seeded · Receipts

Reports the lockbox process that creates receipts from bank-provided payment files, listing lockbox receipts and validation errors per bank transmission.

Sample build of the Process Receipts Through Lockbox 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

1,420

Lockbox receipts

$11.40M

Applied

Unapplied/errored

Lockbox	Receipts Created	Applied	Amount	Errors
Sample	Sample	Sample	$1,240,500.00	Sample
—	—	—	$842,150.75	—
Sample	Sample	Sample	$96,400.00	Sample
—	—	—	$1,005,233.10	—
Sample	Sample	Sample	$58,720.40	Sample
Sample	Sample	Sample	$1,240,500.00	Sample

AI Analyst · active

reading

The report reports the lockbox process that creates receipts from bank payment files.

flag

96 lockbox receipts couldn't auto-apply — cash received from the bank but not matched to invoices.

root cause & next step

Improve the lockbox matching on invoice references; unmatched lockbox cash is the biggest source of unapplied receipts.

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_CASH_RECEIPTS_ALL	dimension	dimension
AR_RECEIVABLE_APPLICATIONS_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_PAYMENTS_INTERFACE_ALL	Setup / configuration table — joined for reference, not exposed for analytics
AR_CASH_RECEIPTS_ALL	25	9
AR_RECEIVABLE_APPLICATIONS_ALL	35	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 Unmatched lockbox lines are the pain point; the build adds auto-match scoring and an exception desk for the remainder. Irvine rebuilds these on your data.