Finance IT has moved beyond batch processing for many use cases. Real-time systems enable faster decisions, better risk management, and improved customer experiences.

But not everything needs to be real-time. Here’s when it matters and how to build it.

When real-time matters

Real-time processing adds complexity and cost. Use it when:

Risk management: Detecting fraud, monitoring exposure, and triggering alerts require immediate response.

Trading systems: Market data, order execution, and position updates must be current.

Customer experience: Payment processing, balance updates, and transaction notifications benefit from immediacy.

Regulatory reporting: Some regulations require near-real-time reporting of certain transactions.

Operational efficiency: Real-time reconciliation and exception handling reduce manual work.

Streaming data architecture

Real-time finance systems typically use event streaming:

Event sources: Trading platforms, payment processors, core banking systems emit events as transactions occur.

Stream processing: Kafka, Pulsar, or cloud-native services (Kinesis, Event Hubs) handle event ingestion and distribution.

Processing engines: Flink, Spark Streaming, or cloud services process events, apply business logic, and update state.

Sinks: Processed events update databases, trigger alerts, or feed downstream systems.

State management

Real-time systems need to maintain state:

In-memory stores: Redis or Hazelcast for fast lookups of current positions, balances, or risk metrics.

Event sourcing: Store events as the source of truth, derive current state by replaying events.

CQRS patterns: Separate write models (event streams) from read models (optimized for queries).

Checkpointing: Regularly save state to durable storage for recovery and consistency.

Consistency and correctness

Financial systems must be correct:

Exactly-once processing: Ensure events are processed exactly once, even with failures and retries.

Ordering guarantees: Process events in order when sequence matters (e.g., account balance updates).

Idempotency: Design operations to be safe if retried.

Reconciliation: Regularly reconcile real-time systems with batch systems to catch discrepancies.

Latency optimization

Minimize processing latency:

Co-location: Place processing close to data sources to reduce network latency.

Parallel processing: Process independent events in parallel while maintaining ordering where needed.

Caching strategies: Cache frequently accessed reference data to avoid database lookups.

Async processing: Use asynchronous processing for non-critical paths while keeping critical paths synchronous.

Monitoring and observability

Real-time systems need different monitoring:

Latency metrics: Track end-to-end latency from event generation to final state update.

Throughput metrics: Monitor events per second and processing capacity.

Backpressure handling: Detect when processing can’t keep up and implement backpressure strategies.

Data quality: Monitor for missing events, duplicates, or out-of-order events.

When batch is still better

Real-time isn’t always the answer:

Historical analysis: Complex analytics over large time windows are often better as batch jobs.

Cost optimization: Batch processing can be more cost-effective for non-time-sensitive workloads.

Complex calculations: Heavy computations that don’t need immediate results are better as batch.

Regulatory reporting: Many reports are required on a schedule (daily, monthly) and don’t need real-time.

The key is matching processing patterns to business requirements. Real-time processing is powerful, but it adds complexity. Use it where it provides real value, and use batch where it’s sufficient.