`Real-Time Revenue Leak Detector (SaaS or Fintech)

In SaaS and Fintech businesses, even minor misconfigurations or billing errors can lead to massive revenue leaks — unnoticed billing mismatches, failed transactions, usage underreporting, or incorrect pricing logic. Existing solutions either work in batches (not real-time), require complex setup, or lack intelligent anomaly detection.We wanted to build a plug-and-play, serverless, real-time solution that detects revenue leaks as they happen, preventing losses before they accumulate.Real-Time Revenue Leak Detector continuously monitors: Billing transactions and logs Pricing rules versus actual charges User subscription events versus payment records Anomalies in revenue flow (e.g., sudden drops, silent churn) API/usage mismatches (e.g., high usage, no billing) It triggers alerts and logs anomalies in real time. It can also roll back changes, flag accounts, or trigger escalation workflows through AWS EventBridge or SNS.We used a fully serverless AWS architecture: AWS Lambda — core real-time compute logic Amazon EventBridge — for capturing and responding to system events (subscriptions, payments, usage) Amazon DynamoDB — fast, scalable anomaly record storage Amazon CloudWatch Logs — for observability and tracking leak patterns AWS SNS / Slack Integration — for instant alerting AWS Step Functions — to manage multi-step anomaly workflows Anomaly Detection — embedded lightweight Python model (combining statistical rules and basic machine learning) What we learned,Gained deeper insight into AWS Lambda and EventBridge orchestration,Learned to embed ML-in-the-loop inside stateless serverless functions ,Improved our understanding of stream decoupling for financial observability,Adopted better practices in monitoring and observability for serverless applications.

Built With

• amazon-web-services
• boto3
• javascript
• python
• scikit-learn
• sdk
• stripe/razorpay/chargebee