Using ClickHouse for Fraud Detection in Spring Boot

December 20, 2025

#1

ClickHouse is ideal for fraud detection thanks to its ability to handle large datasets and run complex queries at high speed in Spring Boot apps.

1. Set Up ClickHouse Table for Transactions

Create a table optimized for analytics:

CREATE TABLE transactions (
  id UUID DEFAULT generateUUIDv4(),
  user_id String,
  amount Float64,
  location String,
  timestamp DateTime DEFAULT now()
) ENGINE = MergeTree()
ORDER BY timestamp;

This structure allows efficient queries for detecting anomalies.

2. Insert Transaction Data via Spring Boot

Use a simple repository:

@Repository
public interface TransactionRepo extends JpaRepository<Transaction, UUID> {}

Insert data from your service layer when transactions occur.

3. Write Fraud Detection Queries

Detect unusual spending patterns:

SELECT user_id, COUNT(*) AS tx_count
FROM transactions
WHERE timestamp > now() - INTERVAL 1 HOUR
GROUP BY user_id
HAVING tx_count > 10;

This flags users with suspiciously high activity.

4. Integrate with Spring Boot Services

Run fraud queries in scheduled jobs:

@Scheduled(fixedRate = 60000)
public void detectFraud() {
    jdbcTemplate.query("SELECT ...", rs -> {
        // Handle suspicious activity
    });
}

5. Add Real-Time Alerts

Combine detection logic with Spring Boot’s notification system (email, Slack, or logging) to act on fraud attempts quickly.

6. Best Practices

• Use partitioning for time-based data.
• Index fields like user_id and timestamp.
• Keep queries optimized with proper schema design.