Detect Fraud in Real Time, Not After the Fact
Batch fraud detection finds losses. Real-time fraud detection prevents them. With Ververica fraud is identified, scored, and blocked before the transaction clears.
Batch Fraud Detection
Is a Post-Mortem
Most banks still detect fraud in overnight batch runs. By morning, the money is gone. Alerts fire on transactions that cleared 8 hours ago. Investigation teams chase cold trails.
The global cost of financial fraud exceeded $485 billion in 2025. Batch processing does not detect fraud. It documents it.
What stops you from real-time?
Core Capabilities
Pattern Detection
Identify complex fraud patterns across millions of concurrent sessions. Multi-hop transaction chains, velocity checks, and behavioral anomalies. All evaluated in real time against streaming data.
ML Model Scoring
Execute machine learning models inline with transaction streams. Real-time feature engineering, model inference, and score aggregation. No batch pre-computation. Models score live data at sub-10ms latency.
Complex Event Processing
Correlate events across accounts, channels, devices, and time windows. Detect coordinated attacks that span multiple entities. Windowed pattern matching at 6.9B records/sec throughput.
Adaptive Rules Engine
Deploy and update fraud rules without restarting pipelines. Business analysts define rules. The platform applies them to live streams instantly. No deployment cycles. No downtime.
Key Reasons To choose Ververica
Why Ververica
Sub-10ms Latency
From transaction ingestion to fraud decision in under 10 milliseconds. Measured in production across tier-1 banks during peak volumes.
6.9B Records/Sec
The VERA engine sustains 6.9 billion records per second. Black Friday, month-end, market volatility. Throughput does not degrade.
Exactly-Once Processing
Zero duplicate alerts. Zero missed transactions. Every event is processed once and only once, even during node failures and cluster rebalancing.
Real-Time ML
Models execute inline with the stream. No round-trip to external scoring services. Feature computation and inference in the same pipeline at full throughput.
Under the Hood
Ververica's fraud detection runs on the VERA engine, a proprietary extension of Apache Flink built for stateful stream processing at extreme scale. The engine maintains per-session state across billions of concurrent keys with RocksDB-backed state management and incremental checkpointing. State snapshots occur without pausing processing, ensuring zero-latency impact during fault tolerance operations.
ML models deploy as user-defined functions within the Flink job graph. Feature engineering and inference execute in the same JVM process as the stream operator. This eliminates network round-trips to external model servers. Models accept TensorFlow SavedModel, ONNX, and PMML formats. Hot-swapping models requires no pipeline restart.
The complex event processing layer uses a custom NFA (nondeterministic finite automaton) implementation optimized for high-cardinality pattern matching. It evaluates thousands of concurrent patterns across sliding, tumbling, and session windows simultaneously. Combined with Ververica's adaptive rule engine, fraud analysts can modify detection logic in production without engineering involvement.
Related Solutions
AML Monitoring
Continuous anti-money laundering monitoring with real-time behavioral analysis and automated SAR triggers.
Risk Management
Real-time exposure tracking, VaR calculation, and automated limit breach alerts across all positions.
Real-Time Payments
Process instant payments with sub-10ms latency across ISO 20022, SEPA Instant, and FedNow rails.
Frequently Asked Questions
01How fast can Ververica detect fraud?
How fast can Ververica detect fraud?
Ververica detects fraud in under 10 milliseconds from transaction ingestion to decision. This includes feature computation, ML model inference, rule evaluation, and pattern matching. The latency holds at peak throughput of 6.9B records/sec across production deployments.
02What machine learning frameworks are supported?
What machine learning frameworks are supported?
Ververica supports TensorFlow SavedModel, ONNX, and PMML model formats for inline inference. Models execute within the stream processing pipeline with no external service calls. Hot-swapping models in production requires no pipeline restart and causes no processing gaps.
03How does this differ from batch fraud detection?
How does this differ from batch fraud detection?
Batch fraud detection runs on historical data, typically hours or days old. Ververica processes transactions as they occur. Fraud is detected and blocked before settlement. The difference is between documenting losses and preventing them. Real-time detection cuts fraud losses by 60% or more.
04Can fraud rules be updated without downtime?
Can fraud rules be updated without downtime?
Yes. The adaptive rules engine accepts rule changes in production without pipeline restarts. Business analysts define rules through a management interface. Changes propagate to the processing layer in seconds. No engineering deployment cycles required.
05Does Ververica guarantee exactly-once processing for fraud detection?
Does Ververica guarantee exactly-once processing for fraud detection?
Ververica guarantees exactly-once semantics across the entire fraud detection pipeline. Every transaction is evaluated once and only once. No duplicates. No gaps. This holds during node failures, network partitions, and cluster rebalancing operations.
Stop Fraud
Before It Clears
Every millisecond between transaction and detection is exposure. The VERA engine closes that gap to under 10ms. Production-proven across tier-1 banks globally.