Cash in Transit Report

Cash Management◆ Seeded · Cash transit

For a bank account, the transactions remitted to the bank but not yet cleared as of a date — payments and receipts in flight — excluding voided and reversed items.

Sample build of the Cash in Transit 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

118

In-transit items

$2.60M

In transit

Over 5 days

Bank Account	Transaction	Type	Remit Date	Amount	Days In Transit
1000-2100-000	Sample	Standard	2026-04-30	$1,240,500.00	Sample
1000-5400-000	—	Corporate	2026-03-31	$842,150.75	—
1000-1410-000	Sample	Standard	2026-02-28	$96,400.00	Sample
2000-2100-000	—	Default	2026-01-31	$1,005,233.10	—
1000-6300-000	Sample	Standard	2025-12-31	$58,720.40	Sample
1000-2100-000	Sample	Standard	2026-04-30	$1,240,500.00	Sample

AI Analyst · active

reading

The report reads remitted-but-uncleared payments and receipts as of the date.

flag

19 items have been in transit over five days — the bank hasn't cleared them, or they were never actually sent.

root cause & next step

Chase the bank or void and reissue; an item stuck in transit overstates cash and hides a failed payment.

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_CHECKS_ALL	dimension	dimension
AR_CASH_RECEIPTS_ALL	dimension	dimension
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.

Cash Management 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
CE_STATEMENT_RECONCILS_ALL	Setup / configuration table — joined for reference, not exposed for analytics
AP_CHECKS_ALL	44	6
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 Rebuilt as an aging of in-transit items so stale uncleared payments and receipts surface for follow-up. Irvine rebuilds these on your data.