GUIDE

Real-Time Fraud Detection with Streaming SQL

Q: Can RisingWave handle the throughput needed for fraud detection?

Yes. RisingWave is designed for high-throughput streaming workloads and can process millions of transactions per second with sub-second latency. It scales horizontally by adding compute nodes, making it suitable for financial institutions processing large transaction volumes.

Q: How does RisingWave compare to dedicated fraud detection platforms?

Dedicated fraud platforms are often black-box systems with proprietary rule languages. RisingWave gives your fraud team full control using standard SQL — they can write, test, and deploy new detection rules in minutes. It also integrates with existing ML scoring services for hybrid rule+ML approaches.

Q: Can I combine rule-based and ML-based fraud detection in RisingWave?

Yes. Use RisingWave materialized views for rule-based pattern detection (velocity checks, amount thresholds, geo-anomalies) and call external ML scoring APIs via user-defined functions. Combine both signals in a final risk score materialized view.

Q: Does RisingWave support the windowed aggregations needed for fraud detection?

Yes. RisingWave supports tumbling windows, hopping windows, session windows, and sliding window aggregations — all in standard SQL. These are essential for computing velocity metrics like 'transactions in the last 5 minutes' or 'unique merchants in the last hour.'

Detect fraud as it happens using streaming SQL. RisingWave continuously evaluates transaction patterns, computes risk scores, and triggers alerts in milliseconds — replacing batch fraud systems that detect fraud hours too late.

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Detection Latency

Evaluate every transaction against fraud rules in sub-second time — not hours later in a batch job

SQL

Rule Deployment

Deploy new fraud detection rules instantly with a single CREATE MATERIALIZED VIEW statement

Windowed

Pattern Detection

Tumbling, hopping, and session windows for computing velocity metrics across time periods

Scalable

Millions TPS

Process millions of transactions per second — scale horizontally by adding compute nodes

The Problem

Why do batch-based fraud detection systems fail?

Batch-based fraud detection systems run on schedules — hourly, daily, or at best every few minutes. During the gaps between runs, fraudulent transactions complete undetected, funds leave the system, and stolen credentials are used across multiple accounts. By the time a batch job flags suspicious activity, the damage is done and recovery is costly or impossible.

Factor	Batch Fraud Detection	Real-Time (RisingWave)
Detection Latency	30 min — 24 hours	< 1 second
Fraud Window	Entire batch interval	Milliseconds
Loss per Incident	$50K — $500K	$100 — $5K (caught early)
Pattern Detection	Single-window only	Cross-window, continuous
Rule Deployment	Next batch cycle	Immediate (SQL change)
Compliance	Increasingly non-compliant	PSD2/PCI-DSS ready

→A fraudster who compromises an account at 2:05 AM has until the next batch run to drain funds
→Velocity-based patterns (rapid small charges before a large one) span multiple batch windows and are invisible to each individual run
→Cross-account fraud rings coordinate activity that only becomes apparent when analyzed as a continuous stream
→Alert fatigue increases when batch systems produce bulk alerts all at once instead of real-time prioritized signals
→Regulatory requirements (PSD2, PCI-DSS) increasingly mandate real-time transaction screening

Detection Patterns

How does RisingWave enable real-time fraud detection with SQL?

RisingWave ingests transaction streams from Kafka or payment gateways, evaluates fraud rules as continuously updating materialized views, and outputs risk scores and alerts in real-time. Fraud analysts write detection logic in standard SQL — no Java, no custom DSLs, no ML pipeline infrastructure. New rules deploy instantly by creating a new materialized view.

Velocity Checks

Count transactions per card per time window. Flag cards exceeding thresholds like 5 transactions in 2 minutes or 10 unique merchants in 1 hour.

Amount Anomaly Detection

Compare each transaction against the cardholder's historical average. Flag transactions that exceed 3x the rolling 30-day average amount.

Geo-Velocity Analysis

Track transaction locations and timestamps. Flag physically impossible travel — two transactions 500 miles apart within 10 minutes.

Cross-Account Correlation

Join transaction streams across accounts to detect coordinated fraud rings sharing devices, IPs, or shipping addresses.

Implementation

What fraud detection patterns can you implement with streaming SQL?

Streaming SQL in RisingWave supports every major fraud detection pattern — from simple threshold rules to complex multi-stream correlations. Each pattern is a materialized view that updates in real-time, can be queried directly by fraud analysts, and can sink alerts to downstream systems like Kafka, Slack webhooks, or case management tools.

→Deploy new fraud rules by running a single CREATE MATERIALIZED VIEW statement — no code deploy, no downtime
→Test rules against historical data by replaying Kafka topics through the same SQL logic
→Monitor rule effectiveness in real-time by querying materialized views that track true positive and false positive rates
→Scale detection capacity horizontally by adding RisingWave compute nodes — no re-architecture needed

Frequently Asked Questions

Can RisingWave handle the throughput needed for fraud detection?

How does RisingWave compare to dedicated fraud detection platforms?

Can I combine rule-based and ML-based fraud detection in RisingWave?

Does RisingWave support the windowed aggregations needed for fraud detection?

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