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 Teams using Authorize.net and similar gateways where payments/refunds flow through HighLevel Healthcare clinics and any organization under BAA/HIPAA Agencies supporting HIPAA-bound clients 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