Tens of thousands of orders per month and increasingly a target for fraudsters
Using a homegrown system that identified suspicious orders with a value above a certain amount or were from certain geographies, among other rules
Rules-based are ineffective once fraudsters start changing their tactics
Costs quickly mounted into rapidly growing fraud problem
92% of orders Sift Science identified as fraudulent were indeed fraudulent
8x return on investment on their monthly fees
Delivering world-class accuracy
Small but nimble, the fraud team at JackThreads successfully tackled their initial trickle of chargebacks using a homegrown system that identified suspicious orders with a value above a certain amount or were from certain geographies, among other rules. The team's elation soon turned to concern as the trickle of chargebacks became a deluge.
JackThreads discovered that rules-based systems cast a wide net and are ineffective once fraudsters start changing their tactics, forcing the team to play cat-and-mouse to adjust rules while reviewing a growing list of suspicious orders. Costs quickly mounted.
JackThreads quickly settled on Sift Science after researching potential solutions. A quick sign-up process, a well-documented API their developers loved, a powerful but intuitive dashboard that made manual reviews easy and, most importantly, a machine-learning based approach that delivered world-class accuracy made their decision simple.
The results speak for themselves. Nearly 92% of orders Sift Science identified as fraudulent were indeed fraudulent - the industry average is 25%.
By using Sift Science, JackThreads saves thousands of dollars a month in chargeback costs and realizes an 8x return on investment on their monthly fees.