Building a Secure Payment Processing Platform

FinTrack had a working payment processing prototype but needed it production-ready for their Series B — PCI-compliant, handling 50,000+ daily transactions, with 99.99% uptime guarantees. Their two-person engineering team had built the MVP but didn't have the infrastructure or security expertise to take it to enterprise grade. They needed it done in 10 weeks.

The Situation

FinTrack is a fintech startup that simplifies payment processing for small merchants. Their Series A product was solid — merchants loved the UX — but it was built for hundreds of transactions, not thousands. Their Series B investors required proof that the platform could scale to enterprise volumes with proper security before releasing the next funding tranche.

What They Needed

• PCI DSS Level 1 compliance — their payment handling touched card data in ways that wouldn't pass an audit. They needed the architecture restructured so sensitive data never touches their servers
• 50,000+ daily transactions — the current system processed about 2,000/day and started dropping requests above 5,000
• 99.99% uptime SLA — their existing single-server deployment had no redundancy, no failover, and no disaster recovery
• Fraud detection — investors wanted real-time transaction scoring before they'd commit, but FinTrack had zero ML/fraud experience
• 10-week deadline — their Series B term sheet expired if they couldn't demonstrate the capability by a specific date

The Stakes

$8M in Series B funding was contingent on passing a technical due diligence audit that would specifically test scaling, security, and compliance. Miss the deadline, lose the round.

Our Approach

We assigned a team of four engineers — two focused on infrastructure and security, two on application features and performance. We worked in parallel tracks to hit the 10-week deadline.

Track 1: Security & Compliance (Weeks 1–6)

1. PCI architecture redesign — Moved all card data handling to a tokenisation layer (Stripe Connect) so FinTrack's servers never see raw card numbers. This reduced their PCI scope from SAQ D (hardest) to SAQ A (simplest)
2. Encryption at rest and in transit — Implemented AES-256 encryption for stored merchant data, TLS 1.3 for all API communication, and encrypted database connections
3. Access controls — Role-based access with audit logging for every data access. Built an admin dashboard showing who accessed what and when
4. Penetration testing — Engaged a third-party security firm in week 5 to validate our work. They found zero critical issues and two low-severity items we fixed in a day
5. Compliance documentation — Produced the security policies, incident response plan, and data handling procedures required for the PCI audit

Track 2: Scale & Reliability (Weeks 1–8)

6. Microservices decomposition — Split the monolith into four services: payment ingestion, transaction processing, reconciliation, and merchant API. Each scales independently
7. Database optimisation — Migrated from a single PostgreSQL instance to a primary-replica setup with connection pooling. Added indexes that reduced the settlement query from 30s to 200ms
8. Queue-based processing — Moved transaction processing from synchronous to asynchronous with Redis queues and dead-letter handling, so traffic spikes queue gracefully instead of failing
9. Multi-AZ deployment — Deployed across three AWS availability zones with automatic failover. Database replication with <1s lag ensures no data loss on primary failure
10. Load testing — Simulated 100,000 transactions/day (2x target) for 72 hours. Zero dropped requests, average latency under 200ms

Track 3: Fraud Detection (Weeks 4–9)

11. Transaction scoring — Built a rules-based fraud detection engine that scores each transaction in real-time based on velocity, geography, device fingerprint, and historical patterns
12. Merchant dashboard — Added a fraud overview panel showing flagged transactions, risk trends, and the ability to manually review and approve/decline holds
13. Automated blocks — High-confidence fraud (stolen card databases, impossible geography) blocked automatically; medium-confidence held for merchant review

Results

Metric	Before	After
Daily transaction capacity	~5,000 (then failures)	50,000+ sustained
Uptime	~99.5% (unmonitored)	99.99% (monitored, SLA-backed)
Payment processing time	2.4 seconds avg	340ms avg (-86%)
PCI compliance level	Non-compliant	PCI DSS Level 1 certified
Security incidents since launch	Unknown	Zero in 12 months
Fraud detection	None	Real-time scoring, <50ms latency

The Outcome

FinTrack passed their technical due diligence with flying colours. The auditor specifically called out the infrastructure design and security posture as "mature beyond the company's stage." The $8M Series B closed on schedule.

Twelve months later, FinTrack processes over 120,000 daily transactions across 3,000 merchants with zero security incidents and one nine (99.99%) uptime. Their fraud detection system has blocked $2.3M in fraudulent transactions.

We continue to work with FinTrack on a retainer basis — performance optimisation, new payment method integrations, and scaling for their expansion into the UK market.

Key Takeaway

Technical due diligence isn't about having perfect code — it's about demonstrating that your team has thought through the hard problems (security, scale, reliability) and built solutions that prove you can handle enterprise volumes. A focused 10-week engagement with the right expertise can bridge the gap between "promising MVP" and "fundable platform."