Edge Computing and Stream Processing: Processing Data Where It's Generated

FinTech applications require real-time data processing for payments, risk management, compliance monitoring, and transaction analytics. Streaming SQL handles these workloads with sub-100ms latency.

FinTech Streaming Use Cases

Payment Processing Analytics

CREATE MATERIALIZED VIEW payment_metrics AS
SELECT payment_method, currency,
  COUNT(*) FILTER (WHERE ts>NOW()-INTERVAL '5 minutes') as txns_5min,
  SUM(amount) FILTER (WHERE ts>NOW()-INTERVAL '1 hour') as volume_1h,
  COUNT(*) FILTER (WHERE status='failed')::DECIMAL/NULLIF(COUNT(*),0) as failure_rate
FROM payments WHERE ts>NOW()-INTERVAL '1 hour'
GROUP BY payment_method, currency;

Real-Time Risk Scoring

CREATE MATERIALIZED VIEW risk_scores AS
SELECT user_id,
  COUNT(*) FILTER (WHERE ts>NOW()-INTERVAL '1 hour') as txn_velocity,
  COUNT(DISTINCT country) FILTER (WHERE ts>NOW()-INTERVAL '1 hour') as country_diversity,
  SUM(amount) FILTER (WHERE ts>NOW()-INTERVAL '24 hours') as daily_volume,
  CASE WHEN COUNT(DISTINCT country)>3 OR COUNT(*)>20 THEN 'HIGH'
       WHEN COUNT(DISTINCT country)>1 OR COUNT(*)>10 THEN 'MEDIUM'
       ELSE 'LOW' END as risk_level
FROM transactions WHERE ts>NOW()-INTERVAL '24 hours'
GROUP BY user_id;

Frequently Asked Questions

Is RisingWave suitable for payment processing?

RisingWave is suitable for payment analytics, risk monitoring, and compliance — not as the payment processor itself. It provides the real-time analytical layer alongside your payment infrastructure.

Streaming Data in FinTech: From Payments to Risk Management