The Hidden Cost of Payment Instability in PropTech
Real estate transactions involve large sums, recurring payments, and complex fee structures — making payment processing failures disproportionately costly. When a payment gateway goes down or routes a transaction sub-optimally, the impact compounds: failed rent collection, delayed closings, and manual reconciliation overhead that scales with portfolio size. VSBD's ML Payment Gateway Cascade project tackled this problem head-on for a real estate financial platform — with measurable results.
The Problem: Fragmented Gateway Landscape
Modern real estate platforms typically route payments through multiple gateway providers for redundancy and geographic coverage. Each provider has different uptime characteristics, fee structures, and performance profiles across transaction types. Without intelligent routing, platforms typically rely on static configuration — which means they can't adapt when a provider degrades, and they leave money on the table by not optimizing route selection based on real-time performance data.
The ML Solution: Cascade Routing
The VSBD team designed a cascade routing system based on an ML model trained on historical transaction data across all gateway providers. The model learns:
Provider success probability per transaction type, amount range, and geographic region
Latency profiles to optimize for time-sensitive transaction types
Fee optimization across provider pricing models for different payment instruments
Anomaly signals that indicate a provider is degrading before it fully fails
The cascade mechanism: if the primary route fails or the model predicts a low success probability, the system automatically routes to the next-best provider — without manual intervention.
The Data Pipeline Architecture
The ML model is only as good as the data feeding it. The VSBD team built supporting data stream pipelines that:
Ingest real-time transaction outcomes from all gateway providers
Normalize provider-specific response formats into a unified schema
Trigger automated model evaluation when performance metrics drift beyond thresholds
Feed a CI/CD pipeline for model updates, enabling rapid iteration without manual deployment
Quality Engineering: Testing ML Systems
Testing payment routing is harder than testing conventional business logic — the behavior is probabilistic, and edge cases can have significant financial consequences. The QA approach included:
Multi-stream integration testing simulating production-like transaction volumes across all provider integrations
Regression suites that verify cascade behavior under simulated provider degradation scenarios
E2E testing of the full payment lifecycle including refund and dispute flows
Load testing to validate cascade routing under peak transaction volumes
Delivered Within Budget: €370k, 6 Months
The project was delivered within the fixed-cost business model commitment: €370k budget, 6-month timeline. The team included a Project Manager, 2 Data Engineers, 1 Data Scientist, 2 Data Analysts, 1 Manual QA, and 1 Automation QA — a lean, focused composition for a well-scoped problem.
Results
40% reduction in payment gateway fluctuations
95% reduction in human error through elimination of manual financial data manipulation
50% decrease in time-to-market for new payment capabilities
25% reduction in support costs through streamlined internal processes
Improved corporate transparency by eliminating manual financial data manipulation
For PropTech platforms handling high payment volumes, ML-powered routing is not a luxury — it's the difference between reliable revenue collection and expensive operational overhead.
Originally published on the VSBD blog.
