improvement over industry standards within one year of Couchbase
saved with Couchbase
of fraudulent transactions caught
- 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
- 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
Revolut Uses Machine Learning to...
See how Revolut combines machine learning with Couchbase Server's speed, agility, and scalability to monitor card transactions and reduce financial fraud.
Couchbase and Revolut Win FS...
Revolut developed Sherlock, a machine learning-based card fraud prevention system, to counter the growing threat of financial fraud.
Building a State-of-the-Art Card Fraud...
Sherlock is our card fraud prevention system based on machine learning. It continuously and autonomously monitors Revolut users' transactions.
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