ACH/RPPS Payment Platform Case Study

The challenge

A leading US-based FinTech company required a highly secure, scalable payment architecture to power ACH (Automated Clearing House) and RPPS (Retail Payment Processing Service) transactions. The existing platform could not meet growing demands across five critical dimensions:

⚠️ No AI-driven fraud detection. The platform lacked proactive identification of suspicious transactions. Rule-based filters were missing patterns that ML models would catch in real time, leaving high-volume payment flows exposed to emerging fraud vectors.

⚠️ Strict financial compliance and data security gaps. ACH and RPPS standards require rigorous audit trails, data encryption, and transaction integrity. The existing architecture did not satisfy NACHA compliance requirements or PCI-DSS expectations for sensitive financial data.

⚠️ High-volume transaction processing bottlenecks. Processing thousands of money transfers and bill payments daily, the platform experienced queue congestion and database lock contention - causing reconciliation errors and customer-facing delays during peak periods.

⚠️ Unacceptable API latency. Payment confirmation and real-time transaction status APIs were exceeding acceptable latency thresholds, degrading the end-user experience for both payers and payees relying on instant confirmation.

⚠️ Architecture not future-ready. The monolithic design could not absorb new payment rails, additional financial partners, or international transaction growth without significant re-engineering effort.

Platform architecture

ACH / RPPS Platform - Layer Stack

🔐 OAuth 2.0 + JWT Authentication Layer Security

🤖 AI / ML Fraud Detection & Anomaly Engine AI / ML

⚡ Apache Kafka - Real-time Event Streaming Real-time

🏗️ Java + Spring Boot Microservices Orchestration Scalable

🗄️ PostgreSQL (HA) + Redis Cache Layer HA

✅ ACH / RPPS Compliance & Audit Layer Compliant

Our solution

OKRUTI architected a microservices-based FinTech platform for secure, scalable payments - integrating AI-powered fraud intelligence, anomaly monitoring, and predictive analytics at every critical layer.

→ AI-Powered Fraud Intelligence: Implemented ML models trained on transaction patterns for real-time anomaly detection and proactive fraud prevention. The system classifies transactions in milliseconds - flagging suspicious patterns before payment execution rather than in post-processing batch reviews.

→ Kafka Event Streaming Architecture: Replaced synchronous payment processing with an event-driven Kafka pipeline. Each payment event is published, consumed, and acknowledged asynchronously - eliminating queue congestion and ensuring fault-tolerant, exactly-once processing semantics across all ACH and RPPS rails.

→ Java & Spring Boot Microservices: Decomposed the monolith into domain-specific services - Payment Initiation, Fraud Analysis, Compliance Validation, Settlement, and Notification - each independently deployable, scalable, and fault-isolated. A failure in notification does not affect payment processing.

→ PostgreSQL with High Availability: Managed complex financial data with read replicas, connection pooling, and optimised transaction queries. Implemented advisory locks to resolve concurrency issues and eliminate the reconciliation errors seen in the original architecture.

→ OAuth 2.0 + JWT Authentication: Enforced token-based security at every API boundary with short-lived, scoped JWTs. Refresh token rotation and token revocation lists prevent replay attacks and ensure that compromised tokens cannot be reused.

→ ACH / RPPS Compliance Layer: Built a dedicated compliance microservice implementing NACHA file format validation, return code handling, and full audit trail generation. All transactions produce immutable audit logs enabling clean regulatory examinations and real-time compliance reporting.

→ Optimised Payment APIs: Redesigned all external-facing APIs with connection keep-alive, response caching for idempotent requests, and async acknowledgement patterns - bringing payment confirmation latency well within real-time thresholds.

Results achieved

3×

Transaction throughput - same infrastructure, 3× the volume handled post-redesign

Zero

Downtime post-launch - zero-downtime SLA maintained through phased cutover

Real-time fraud detection - ML models identifying suspicious transactions in under 20ms

100%

ACH & RPPS compliance - clean NACHA audit, all return codes handled, full trail

OKRUTI understood ACH and RPPS compliance requirements before we even explained them. The platform went live ahead of schedule and hasn't had a critical incident since. Their fraud detection integration alone saved us significantly in the first quarter.

- Head of Engineering, Leading US Payment Platform

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