Fraud Detection

Real-Time Fraud Detection with Streaming SQL

Evaluate fraud rules continuously over Kafka transaction streams using SQL. RisingWave detects multi-signal fraud patterns within milliseconds, with no rule engine, no batch jobs, no Flink cluster.

Start FreeReal-Time Alerting
Sub-Second
Detection Latency
Fraud rules evaluate continuously as transactions arrive, flagging suspicious activity within milliseconds before funds settle
SQL
Rule Engine
Express velocity checks, geo-anomalies, and multi-signal fraud patterns in standard SQL without custom rule engine code
Multi-Signal
Pattern Detection
Correlate transaction amounts, velocity, device fingerprints, and account history in a single SQL fraud rule definition
PostgreSQL
Integration Interface
Connect fraud decisions to payment processors, case management systems, and block lists via standard PostgreSQL protocol

Why Streaming Fraud Detection

Why does fraud detection require continuous stream evaluation?

Batch fraud detection reviews transactions after they settle, often hours later. By then the money has moved. Continuous stream evaluation checks every transaction against fraud rules as it arrives, enabling real-time block decisions before funds clear.

FactorBatch / Rule EngineRisingWave
Detection WindowHours after settlementMilliseconds before settlement
Rule EvaluationBatch job or cronContinuous per-event
InfrastructureRule engine + ETL pipelineSingle SQL system
Multi-Signal RulesComplex CEP codeDeclarative SQL joins
  • →Block fraudulent transactions before funds settle rather than issuing chargebacks after
  • →Evaluate velocity checks, device fingerprints, and account history in a single SQL statement
  • →Correlate transaction streams with blacklist tables using stream-table joins updated in real time
  • →Extend fraud rules by writing SQL, not deploying new rule engine configurations

Use Cases

What types of fraud does continuous stream evaluation detect?

Any fraud pattern with a detectable signal in the transaction stream. Velocity-based fraud, geographic anomalies, account takeover indicators, and promotional abuse all produce event-level signals that SQL window aggregations can detect before the transaction completes.

Payment Velocity Fraud

Detect unusually high transaction frequency within rolling time windows, including cards being tested with small amounts before larger fraudulent charges, using SQL window aggregations over the transaction stream

Geographic Anomaly Detection

Flag transactions from locations inconsistent with recent account activity by joining the live transaction stream against account history, triggering alerts when location jumps exceed plausible travel distance

Account Takeover Detection

Correlate login event streams with transaction streams to identify account takeovers: new device, password reset, and high-value transaction within a short window evaluated as a combined SQL rule

Promotional Abuse

Detect coupon stacking, referral fraud, and bonus abuse by evaluating promotional redemption patterns across account streams with SQL aggregations that surface coordinated abuse in real time

How It Works

How does RisingWave evaluate fraud rules over transaction streams?

RisingWave ingests transaction events from Kafka and continuously evaluates SQL-defined fraud rules using materialized views. Fraud signal state (velocity counts, last-seen location, device history) is maintained as incrementally updated SQL results. Your payment processor or fraud case system queries the fraud materialized view to make block or flag decisions per transaction.

  • →Create a Kafka source in RisingWave pointing to your transaction event topic using a SQL CREATE SOURCE statement
  • →Define fraud rules as SQL materialized views with window aggregations: COUNT(*) OVER 1 HOUR per card, geographic distance calculations
  • →Join the transaction stream with blacklist tables and account context using stream-table joins updated in real time
  • →Query the fraud materialized view from your payment gateway to make per-transaction block decisions before settlement
  • →Add new fraud rules by writing SQL SELECT statements, deploying changes without restarting the pipeline

Frequently Asked Questions

How do I detect payment fraud in real time using streaming data?
How does RisingWave compare to a traditional fraud rule engine?
How does RisingWave compare to Apache Flink for real-time fraud detection?
How do I keep fraud blacklists current in real time?

Block fraud before funds settle, not after

Define fraud rules in SQL over your Kafka transaction streams and start making real-time block decisions without a rule engine or Flink cluster.

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