Revenue Leakage Detection System

Augustus

Augustus Chukwu

Revenue Leakage Detection System

Automated system to detect and recover lost revenue in B2B SaaS companies

๐ŸŽฏ What It Does

Automatically detects 4 types of revenue leakage:
Outdated Pricing - Customers paying old rates
Missing Overages - Usage charges not applied
Incorrect Discounts - Unqualified discounts given
Feature Leakage - Premium features on basic plans
Typical Results:
$500K-900K recovered annually
1,900% ROI
90% time reduction vs manual audits

๐Ÿ“Š Key Features

โœ… 100% Coverage - Checks all customers daily, not just a sample
โœ… Real-time Detection - Finds issues within 24 hours
โœ… Smart Prioritization - Severity scoring by revenue impact
โœ… Automated Alerts - Slack notifications for high-value issues
โœ… Recovery Workflow - Built-in investigation and resolution tools
โœ… Prevention Layer - Catches errors before billing runs

๐Ÿ—๏ธ Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ DATA COLLECTION โ”‚
โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚
โ”‚ โ”‚ Stripe โ”‚ โ”‚ Product โ”‚ โ”‚ Contract โ”‚ โ”‚ Usage โ”‚ โ”‚
โ”‚ โ”‚ API โ”‚ โ”‚ API โ”‚ โ”‚ Data โ”‚ โ”‚ Logs โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ”‚ โ”‚ โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ SUPABASE DATABASE โ”‚
โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚
โ”‚ โ”‚ customers | contract_pricing | actual_charges | โ”‚ โ”‚
โ”‚ โ”‚ product_usage | pricing_rules | leakage_detections โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ VALIDATION ENGINE (n8n) โ”‚
โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚
โ”‚ โ”‚ Outdated โ”‚ โ”‚ Missing โ”‚ โ”‚ Incorrect โ”‚ โ”‚
โ”‚ โ”‚ Pricing โ”‚ โ”‚ Overages โ”‚ โ”‚ Discounts โ”‚ โ”‚
โ”‚ โ”‚ Detector โ”‚ โ”‚ Detector โ”‚ โ”‚ Detector โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ”‚ โ”‚ โ”‚ โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ”‚ โ”‚ โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
โ–ผ
โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚ ALERTING & REPORTING โ”‚
โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚
โ”‚ โ”‚ Slack โ”‚ โ”‚ Email โ”‚ โ”‚ Dashboard โ”‚ โ”‚
โ”‚ โ”‚ Alerts โ”‚ โ”‚ Reports โ”‚ โ”‚ (Web) โ”‚ โ”‚
โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

๐Ÿš€ Quick Start

Prerequisites

Supabase account (free tier works)
n8n instance (self-hosted or cloud)
Stripe account with API access
Slack workspace (optional, for alerts)

Installation

1. Clone the repository
git clone https://github.com/Etherlabs-dev/revenue_leakage_system.git
cd revenue_leakage_system
2. Set up Supabase database
# Run the database schema script
psql -h your-supabase-url -d postgres -f database/schema.sql

# Load sample data (optional)
psql -h your-supabase-url -d postgres -f database/sample-data.sql
3. Configure environment variables
cp .env.example .env
# Edit .env with your credentials:
# - SUPABASE_URL
# - SUPABASE_KEY
# - STRIPE_SECRET_KEY
# - SLACK_WEBHOOK_URL
4. Import n8n workflows
# In n8n UI:
# 1. Go to Workflows
# 2. Click "Import from File"
# 3. Import each file from ./n8n-workflows/
5. Configure credentials in n8n
Add Supabase credentials
Add Stripe API key (HTTP Header Auth)
Add Slack webhook URL
6. Test the workflows
# Run each workflow manually first
# Check database for results
# Verify Slack notifications

Usage

The system runs automatically via scheduled workflows:
6:00 AM - Stripe data sync
7:00 AM - Product usage collection
8:00 AM - Outdated pricing detection
9:00 AM - Missing overages detection
10:00 AM - Incorrect discounts detection
5:00 PM - Daily summary report
Manual triggers are also available for testing.

๐Ÿ“ Project Structure

revenue_leakage_system/
โ”œโ”€โ”€ database/
โ”‚ โ”œโ”€โ”€ schema.sql
โ”‚ โ”œโ”€โ”€ sample-data.sql
โ”‚ โ””โ”€โ”€ migrations/
โ”œโ”€โ”€ n8n-workflows/
โ”‚ โ”œโ”€โ”€ 01-stripe-data-sync.json
โ”‚ โ”œโ”€โ”€ 02-usage-collection.json
โ”‚ โ”œโ”€โ”€ 03-detect-outdated-pricing.json
โ”‚ โ”œโ”€โ”€ 04-detect-missing-overages.json
โ”‚ โ”œโ”€โ”€ 05-daily-summary.json
โ”‚ โ””โ”€โ”€ README.md
โ”œโ”€โ”€ docs/
โ”‚ โ”œโ”€โ”€ architecture.md
โ”‚ โ”œโ”€โ”€ detection-logic.md
โ”‚ โ”œโ”€โ”€ setup-guide.md
โ”‚ โ””โ”€โ”€ images/
โ”œโ”€โ”€ scripts/
โ”‚ โ”œโ”€โ”€ test-detection.js
โ”‚ โ””โ”€โ”€ generate-report.js
โ”œโ”€โ”€ .env.example
โ”œโ”€โ”€ LICENSE
โ”œโ”€โ”€ CONTRIBUTING.md
โ””โ”€โ”€ README.md

๐Ÿ”ง Configuration

Detection Rules

Customize detection rules in database/pricing_rules table:
-- Example: Set current pricing for Pro plan
INSERT INTO pricing_rules (
plan_name,
current_base_price,
included_usage,
overage_rates,
features
) VALUES (
'pro',
149.00,
'{"api_calls": 50000, "storage_gb": 100}',
'{"api_calls_per_1000": 5.00, "storage_gb": 2.00}',
'["advanced_analytics", "custom_domains", "api_access"]'
);

Alert Thresholds

Modify severity scoring in workflow functions:
// In n8n Function node
let severity = 'low';
if (leakageAmount > 500) {
severity = 'high'; // Adjust this threshold
} else if (leakageAmount > 100) {
severity = 'medium'; // Adjust this threshold
}

Notification Channels

Configure in n8n Slack node or add email notifications:
// For high severity only
if (severity === 'high') {
// Send Slack alert
// Send email to CFO
}

๐Ÿ“– Documentation

System Architecture - Technical design details
Workflow Logic - How each detection type works
Troubleshooting - Common issues

๐Ÿงช Testing

Run Tests

# Test database schema
npm run test:db

# Test detection logic with sample data
npm run test:detection

# Run full workflow simulation
npm run test:workflow

Sample Test Scenarios

The database/sample-data.sql includes 3 test scenarios:
Customer with outdated pricing - Should pay $149, paying $99
Customer with missing overages - Used 25K API calls, only paid for 10K
Customer with incorrect discount - Has 15% discount without qualification
Expected detections: 3 (one for each scenario)

๐Ÿ“Š Results & ROI

Real Client Results

Case Study: B2B SaaS Company ($5M ARR)
Before:
Manual audits: 20 accounts/month (10% coverage)
Time: 40 hours/month
Leakage found: $50K/month (in audited accounts)
Detection lag: 2-3 months
After:
Automated monitoring: 200 accounts (100% coverage)
Time: 5 hours/month
Leakage found: $65K/month
Detection lag: 1 day
Results:
$663K recovered in year 1
1,907% ROI
35 hours/month saved
Payback in 18 days

ROI Calculator

Annual Leakage Recovered: $XXX,XXX
Annual Time Savings: XXX hours ร— $200/hour = $XX,XXX
Total Annual Value: $XXX,XXX

Implementation Cost: $15,000
Annual Ongoing Cost: $30,000
Total Investment: $45,000

ROI: (Value - Cost) / Cost = X,XXX%

๐Ÿค Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details.

Development Setup

# Fork and clone
git clone https://github.com/yourusername/revenue-leakage-detector.git

# Create feature branch
git checkout -b feature/your-feature

# Make changes and test
npm run test

# Submit pull request

๐Ÿ“ License

This project is licensed under the MIT License - see LICENSE file.

๐Ÿ™‹ Support

๐ŸŒŸ Acknowledgments

Built with:
n8n - Workflow automation
Supabase - Database & real-time
Stripe - Payment processing
Inspired by real problems at my credit card company.

๐Ÿšง Roadmap

Add Chargebee integration
Build React dashboard UI
Add PDF report generation
Create Zapier integration
Add historical trend analysis
Build mobile app for alerts

โญ Star History

Built by Ugo Chukwu Credit Card Company Founder | Financial Automation Specialist
LinkedIn ยท Twitter ยท Website
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Posted Nov 23, 2025

Automated system to detect and recover lost revenue in B2B SaaS companies.