A financial services case study

How Curve slashed chargebacks and streamlined fraud review

Curve is building the world’s first connected finance platform, helping users to send, see and save money effortlessly - all in one place. With their smart card and app, Curve offers a simpler and better way to pay by combining users’ MasterCard and Visa cards into one place.


Drop in chargebacks

More streamlined fraud review

More time saved through automation

Told from the perspective of

Rona Ruthen,Head of Operations
“Although Sift Science wasn’t specifically designed for Curve’s business model, Sift did a good job right away. After some tweaking we reached a great balance of preventing and identifying fraud without impacting good customers.”


Revolutionizing personal payments

The Curve app, paired with a smart bank card, combines all your cards in one and works like a normal bank card anywhere in the world that accepts MasterCard. Users don’t need to open a new bank account and don’t need to wait for weeks — after a few taps and a new Curve card in the post, they’re ready to go.

With Curve, customers benefit from: simpler spending across all their cards, saving money on currency exchange, a smarter and faster way to manage expenses, instant cashback rewards at over 50 leading UK retailers, plus a host of additional security features. Curve users have so far spent £50 million in over 100 currencies, worldwide.


Costly manual resources

With a rapidly growing customer base, thousands of good users, and an increasing spend volume to boot, Curve can also pick up the attention of fraudsters. The ability to quickly add new cards with the Curve app and then use them within moments is of particular interest to malicious users who try to circumvent Curve’s many layers of account authentication, resulting in expensive manual resources for fraud reviews and chargebacks management.

Because their user base is ever-growing, Curve works hard to understand who their customers are and ensure that the card users are who they say they are. Curve has identity verification methods in place, and various teams within the company investigate new and existing users. However, with business growing at such a pace, Curve have taken to preemptively knocking out the growing threat of fraud.


A layered approach to preempt attacks

The Curve team began looking for a fraud tool, focusing on a machine learning solution to avoid having to spend precious resources on building an in-house rules engine from scratch. After reviewing several vendor options, Curve decided to go with Sift Science and quickly integrated the solution. Headed by Product Operations Manager Rona Ruthen, Curve first implemented the basic Sift Score API, and soon began using Sift Science’s findings to assess real-time transactional data.

Within weeks of training the models and learning how to use Sift Science effectively, Rona and her team began to trust the accuracy of Sift Scores. Although Curve’s business model was new to Sift Science, they found that the solution quickly learned what fraud – and not-fraud – looked like for Curve, and was able to pinpoint bad users early and efficiently. Rona and the Curve team now take a layered approach to fraud management, relying on an in-house rules engine to weed out the hard-and-fast business blacklist while utilizing Sift Science to spot the trickier fraudsters, ideally before they even get a Curve card.

“Sift had a very positive effect in days. Other solutions aren’t as real-time as the Sift Science solution.”


More time for a deeper understanding

Although business is booming for Curve, since implementing Sift Science, they’ve only had to add one full-time fraud resource. Instead, Sift Science enables streamlined fraud workflows with a single platform for automation, review, and investigation, and fraud management no longer takes a whole team. Better yet, with Sift Science Curve has seen their chargeback rate drop to 1/6th of what it used to be.

Now, Rona and Curve can stay ahead of bad users by looking at accounts blocked with Sift Science to quickly identify connected bad users. Besides the financial savings in thwarted chargebacks, automating on Sift Scores means that Curve has more time and better data to do in-depth investigations on the fraud cases they encounter.

“The solution is doing a great job! Having some flexibility in defining the workflows and setting them up in a way that allows us to fine-tune it to stop what we want it to stop without impacting good customers is an incredible value.”

A network of trust

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