About Revolut
75%
improvement over industry standards within one year of Couchbase
$3M+
saved with Couchbase
96%
of fraudulent transactions caught
Challenges
- Fraudsters are evolving to beat traditional predetermined fraud detection rules
- A mission-critical application required consistent high availability and high throughput for its rapidly growing customer base
- On average, financial fraud costs institutions between 7-8 cents out of every $100
Outcomes
- Sherlock’s high speed caching enabled machine learning algorithms to continually learn and update rules – catching 96% of fraudulent transactions
- Sherlock evaluates transactions for signs of fraud in under 50 milliseconds for Revolut’s 12+ million customers
- Within the first year in production with Couchbase, a 75% improvement over industry standards saved more than $3M
For our customers, the loss of $100 can mean the difference between a pleasant holiday and an experience filled with frustration and resentment. Couchbase has never failed us or our customers.
Dmitri Lihhatsov Financial Crime Product Owner, Revolut
Industry
Use case
- Fraud detection
- User profile store
- Digital communication
- Caching
Product
Key features
- N1QL
- Multi-dimensional scaling
- Cross datacenter replication
- In-memory database