---
title: "Fraud Detection"
description: "Detect financial fraud in under 10ms with Ververica's stream processing platform. 6.9B records/sec throughput powered by the VERA engine."
lastUpdated: 2026-10-14T09:41:38.000Z
source_url:
  html: "https://www.ververica.com/banking/fraud-detection"
  md: "https://www.ververica.com/banking/fraud-detection.md"
---

# 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.

## How do banks use Apache Flink for real-time fraud detection?

Real-time fraud detection with Ververica scores each card payment, transfer or login while it is still in flight, before the transaction clears. Apache Flink keeps state for every customer, matches multi-step fraud patterns and runs machine learning models inside the stream, and fraud rules can change without restarting the pipeline.

## 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

From transaction ingestion to fraud decision in under 10 milliseconds. Measured in production across tier-1 banks during peak volumes.

The VERA engine sustains 6.9 billion records per second. Black Friday, month-end, market volatility. Throughput does not degrade.

Zero duplicate alerts. Zero missed transactions. Every event is processed once and only once, even during node failures and cluster rebalancing.

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](https://www.ververica.com/developers/flink-incremental-checkpoints). 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](https://www.ververica.com/blog/ing-fraud-detection-apache-flink) requires no pipeline restart.

The [complex event processing layer](https://www.ververica.com/blog/real-time-fraud-detection-using-complex-event-processing) 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](https://www.ververica.com/developers/flink-broadcast-state) without engineering involvement.

## Frequently Asked Questions

### 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.

### 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.

### 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.

### 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.

### 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.