Multi-Processor Payment Reconciliation Automation
Ugo Chukwu
Case Study: Multi-Processor Payment Reconciliation Automation (Stripe + PayPal + Square + ACH)
Overview
Most teams running multiple payment processors spend 10–20 hours/week manually reconciling transactions across Stripe, PayPal, Square, and bank/ACH deposits. That manual work creates missed transactions, duplicates, fee mismatches, and delayed visibility into real cash position.
This project is a production-style reconciliation automation built with n8n + PostgreSQL (portable to any Postgres setup) with Python logic inside n8n code nodes.
Outcome: a daily pipeline that consolidates transactions into a unified ledger, matches them intelligently, flags discrepancies, and optionally prepares/syncs QuickBooks journal entries.
The Problem
Companies using multiple processors typically face:
Manual matching across different exports and dashboards
Confusing settlement timing (charges vs payouts vs bank deposits)
Fees and refunds represented inconsistently across processors
Duplicate and missing transactions during exports/imports
Month-end close stress and low confidence in cash numbers
The Solution
I built an automated reconciliation system that runs daily and produces an auditable reconciliation trail.
What it does
Daily ingestion from:
Stripe (API)
PayPal (API)
Square (API)
ACH/bank deposits (CSV)
Unified ledger in PostgreSQL
Normalization & enrichment (standard fields, computed net/fees, metadata)
Reconcile & match engine with multi-pass logic:
exact matches
fee-aware matching (net vs gross)
refund linking (including partial refunds)
split/partial payments
scoring + threshold to reduce false positives
Exception queue:
missing transactions
fee mismatches
duplicates
unmatched payouts
(Optional) QuickBooks sync:
build journal entries using clearing accounts
track sync state for idempotency
Exception notifications via daily digest
Workflow Design (n8n)
The build is modular and easy to maintain. These are the exact workflows:
01- Ingest Stripe
02 - Ingest PayPal
03 - Ingest Square
04 - Ingest ACH (CSV)
05 - Normalize & Enrich
06 - Reconcile & Match Engine
07 - QuickBooks Sync
08 - Exception Notifications
Screenshots for each workflow live in
docs/screenshots/. Screenshots
Database (PostgreSQL)
The schema is designed for:
raw event storage (auditability)
normalized canonical transactions
reconciliation runs, matches, and exceptions
sync tracking for idempotent accounting updates
This makes the system easy to debug and safe to rerun.
Results / Impact
40+ hours/month saved (replaces manual reconciliation work)
~90% reduction in reconciliation errors (duplicates, missed items, fee mismatches)
Daily cash clarity across all processors
A clean, auditable exception queue for finance review
Deliverables
n8n workflows (import-ready JSON)
PostgreSQL schema (standard SQL)
Seed/test data for demos
Documentation (architecture + workflow screenshots)
Links
GitHub repository:
https://github.com/Etherlabs-dev/multi-processor-reconciliation
If you want this implemented for your business
This template can be adapted to your:
processors (Adyen/Braintree/etc.)
bank feeds
accounting system (QuickBooks/Xero/NetSuite)
chart of accounts and clearing model
If you’re dealing with reconciliation pain, message me with:
processors used
transactions/day
biggest failure mode (fees/refunds/duplicates/missing payouts)
…and I’ll map the fastest path to automate it.
Like this project
Posted Feb 2, 2026
Automate reconciliation across Stripe, PayPal, Square and ACH/bank deposits. Save 40+ hours per month and eliminate 90 % of manual reconciliation errors.
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Timeline
Jan 1, 2026 - Jan 20, 2026
