Modernizing ACH and SEPA Processing with Event-Driven Backends
Refactoring Traditional Batch Payment Systems for Speed and Resilience
Real-time capabilities have grown to be baseline expectation in fintech systems. Yet many banking and financial institutions still rely on legacy, batch-based processing for key payment infrastructures like ACH and SEPA. As customer expectations increase and regulatory scrutiny tightens, refactoring these systems using event-driven architecture becomes a powerful modernization path.
What Are ACH and SEPA?
ACH (Automated Clearing House) is a U.S.-based payment network used for processing electronic payments and money transfers. It’s widely used for payroll, bill payments, direct deposits, and recurring transactions. Traditionally, ACH transactions are processed in batches, often leading to delays in settlement and limited real-time visibility.
SEPA (Single Euro Payments Area) is the European Union’s standard for cross-border euro transactions, enabling consumers and businesses to send and receive euro-denominated payments under a unified set of rules. Like ACH, SEPA processes are also rooted in batch cycles, which limits scalability and real-time control.
Both systems are reliable—but they were built for a time before digital-first expectations and API-driven fintech solutions. Today, modernizing these flows is a priority for banks and payment processors looking to offer faster settlement times, higher transparency, and greater resilience.
The Limitations of Batch-Based Processing
Batch-based payment flows introduce several architectural and operational bottlenecks:
Latency: Transactions may wait hours before processing begins.
Limited observability: It's difficult to trace the status of a transaction in-flight.
Error handling: Failures often surface only after full batch runs, delaying resolution.
Scaling challenges: Processing more transactions typically means longer batch windows or overnight jobs.
These constraints are increasingly incompatible with real-time commerce, instant payouts, and modern embedded finance platforms.
Why Event-Driven Architectures?
Event-driven systems, especially those built on Apache Kafka and Java microservices, offer an elegant solution to batch limitations:
Real-time transaction flows: Every event (e.g., payment initiation, account validation, settlement) is processed as it happens, not in delayed batches.
Improved fault tolerance: Failures can be isolated and retried individually, instead of failing an entire batch.
Better transparency: Events are logged, versioned, and auditable—providing a reliable audit trail.
Scalable design: Event consumers can scale horizontally to meet peak demand without overhauling infrastructure.
From Batch to Streams: A Refactoring Blueprint
Refactoring ACH or SEPA processing systems into event-driven services is a significant architectural shift. Here's a simplified breakdown of the transition path:
1. Decompose Batch Jobs into Domain Events
Start by mapping out your existing batch jobs—file uploads, clearinghouse communications, validation routines—and define discrete business events (e.g., PaymentRequested, PaymentValidated, PaymentCleared). These become the building blocks of your event-driven flow.
2. Design Stateless Microservices
Each business event should be handled by a loosely coupled microservice that listens to a Kafka topic, processes the event, and emits the next event in the chain. Stateless design ensures that services are independently scalable and replaceable.
3. Ensure Idempotency and Ordering
ACH and SEPA systems must handle retries, duplicate events, and out-of-order messages. Your event handlers should be idempotent, and message keys should be used to preserve transaction order where needed.
4. Integrate Legacy Systems
Not all systems can be replaced immediately. Use event adapters that transform legacy batch outputs into events, or replay events into batch-compatible formats when communicating with clearinghouses still reliant on files.
5. Implement Observability and Tracing
Real-time flows require robust monitoring. Use tools like Kafka Streams’ state stores, distributed tracing (e.g., OpenTelemetry), and log aggregation to ensure traceability and error visibility across microservices.
Real-World Example: Streaming SEPA Credit Transfers
Consider a modern bank that wants to process SEPA Credit Transfers (SCT) in near real time. Instead of uploading batch XML files to a clearinghouse, their system breaks down SCT flows into:
SCTInitiated → received from a banking portal or API
SCTValidated → account number and IBAN checks pass
SCTCleared → passed to clearinghouse for final settlement
SCTConfirmed → posted to both sender and recipient
Each step corresponds to a Kafka topic and is processed by an independent Spring Boot microservice. Failures (e.g., invalid account, AML flag) are caught at the corresponding stage and sent to a SCTFailed topic for reconciliation.
Compliance and Fault Tolerance
Modernizing ACH and SEPA flows also improves compliance posture:
Traceability: Each event is stored, providing regulators a full audit trail.
Resilience: If one component fails (e.g., validation), it doesn’t halt the entire payment cycle.
Retry logic: Failed events can be replayed after fixes, instead of rerunning entire batches.
Event-driven architectures also simplify implementing controls like transaction limits, fraud alerts, and KYC verification directly in the stream pipeline, allowing for faster decision-making.
Looking Ahead
While ACH and SEPA aren’t going away anytime soon, the institutions modernizing how they interact with these networks—using streams, services, and events—are positioned to deliver superior user experiences and reduce long-term technical debt.
For fintechs, banks, and payments providers, event-driven architecture isn’t just a performance upgrade. It’s a strategic enabler of future services, real-time integrations, and agile compliance.
