D
David Friedland
Request:
Enable native custom reporting within HighLevel by linking
Contacts
to
Invoices
,
Invoice Line Items/Products
, and
Transactions
(payments + refunds), including
automatic deduction of successful refunds
(including partial refunds). This will allow customers using
third-party gateways
(e.g., Authorize.net) where payments are processed through HighLevel to produce
accountant/GAAP-ready revenue reporting
inside HighLevel, along with executive-level reporting —without exporting to external BI tools.
### Problem
Today, accurate financial reporting requires stitching together multiple objects (products / invoices / transactions) that are not natively joinable in reporting. This produces incorrect “net collections” because
refunds appear as net-positive revenue
or are separated from payment totals, forcing teams into brittle exports, spreadsheets, and custom pipelines. For organizations operating under
BAA/HIPAA constraints
, popular third-party reporting connectors (Coupler/Windsor/Dataddo) are not viable, leaving only
custom AWS/Power BI integrations
and manual reconciliation.
### Who This Helps
  1. Teams using Authorize.net and similar gateways where payments/refunds flow through HighLevel
  2. Healthcare clinics and any organization under
    BAA/HIPAA
  3. Agencies supporting HIPAA-bound clients
  4. Any customer needing accountant-ready collections/refund reporting
### Desired Outcome
Provide a
reporting-ready unified data model (view/dataset)
supporting JOIN-style reporting across:
* Contacts
* Invoices (Paid)
* Invoice line items / Products
* Transactions (Succeeded and Refunds incl. partial refunds; deducted when “successful”)
Expose this dataset to:
* Custom Reports builder
* Dashboard widgets
* CSV exports (accounting workflows)
### Required Use Cases (Examples)
A)
Net Collections (Accountant Report):
total collections
minus successful refunds
, date-filtered, exportable, accurate even when refunds occur later
B)
Gross Collections Net of Refunds Over Time:
day/week/month trendlines; net = payments – successful refunds
C)
Revenue Trendlines by Date Filter:
weekly/monthly rollups (optional filters by location/user)
D)
Top Paying Customers:
ranked by net revenue within a date range
E)
Revenue by Product:
net revenue by product/line item within a date range; supports bundles/packages as line items