Bad Debt Provision Report

Receivables◆ Seeded · Accounting

Estimates bad-debt exposure by applying each customer's percent-collectible to their open balance, listing transaction number, balance due, and the calculated provision amount in the ledger currency.

Sample build of the Bad Debt Provision 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

$24.00M

Open AR

$1.20M

Bad-debt provision

Customers > 90d

Customer	Open Balance	% Collectible	Provision	Aged > 90	Status
Acme Industrial	$1,240,500.00	Sample	Sample	Sample	Open
Northwind Trading	$842,150.75	—	—	—	Posted
Globex Holdings	$96,400.00	Sample	Sample	Sample	Validated
Initech LLC	$1,005,233.10	—	—	—	Open
Umbrella Corp	$58,720.40	Sample	Sample	Sample	Paid
Acme Industrial	$1,240,500.00	Sample	Sample	Sample	Open

AI Analyst · active

reading

The report applies each customer's percent-collectible to their open balance to estimate the bad-debt provision.

flag

$1.2M of provision is concentrated in a handful of customers aged past 90 days — collection risk is clustered, not spread.

root cause & next step

Prioritize collection on the concentrated accounts; a provision driven by a few names is a targeted-collection problem, not a portfolio one.

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_PAYMENT_SCHEDULES_ALL	dimension	dimension
HZ_CUST_ACCOUNTS	dimension	dimension
HZ_PARTIES	dimension	dimension
Open Balance	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
RA_CUSTOMER_TRX_ALL	58	16
AR_PAYMENT_SCHEDULES_ALL	32	6
HZ_CUST_ACCOUNTS	14	43
HZ_PARTIES	81	144

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 The percent-collectible model is coarse; most teams replace it with an aging-based or expected-credit-loss calculation that feeds the allowance journal. Irvine rebuilds these on your data.