Bank Risk Report

Receivables◆ Seeded · Receipts

Identifies receipts currently at risk with the remittance bank, showing remittance batch, receipt, and amount by bank branch and account.

Sample build of the Bank Risk Report — reconciled, and rendered tool-neutral so it runs in Power BI, ThoughtSpot, or Tableau.

Bank Risk Report

Sample build · illustrative

Filters

Period

FEB-26

Ledger

US Primary

Currency

USD

140

At-risk receipts

$3.10M

At risk

Bank branches

Bank Branch	Remittance Batch	Receipt	Amount	Risk Status
Main Branch	Sample	Sample	$1,240,500.00	Open
Downtown	—	—	$842,150.75	Posted
Main Branch	Sample	Sample	$96,400.00	Validated
Westside	—	—	$1,005,233.10	Open
Main Branch	Sample	Sample	$58,720.40	Paid
Main Branch	Sample	Sample	$1,240,500.00	Open

AI Analyst · active

reading

The report identifies receipts currently at risk with the remittance bank — factored with recourse.

flag

$3.1M is at risk, concentrated at one branch — recourse exposure if those receipts dishonor.

root cause & next step

Monitor the concentrated branch; factored-with-recourse receipts are a contingent liability until they clear.

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
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_CASH_RECEIPTS_ALL	25	9
CE_BANK_ACCOUNTS	9	12
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 At-risk exposure is typically rolled into a treasury view alongside maturity and recourse terms. Irvine rebuilds these on your data.