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How Favor Delivery achieved growth while reducing risk

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Overview

Taking over Texas, one delivery at a time

Favor Delivery is a Texas-based on-demand delivery service. Via the mobile app or desktop site, users can place orders for anything from takeout food to last-minute needs from the drugstore, and Runners (delivery assistants) make the delivery in under an hour while keeping users updated every step of the way. Users get what they need in good time and at a great price, while Runners have the freedom to either supplement their income or replace a traditional job by delivering with Favor Delivery. Favor Delivery operates within all major cities in Texas and is continuing to expand throughout the state.

Challenge
  • Increase in chargebacks hindered ability to scale
  • Growing number of fraudulent accounts and ATO attempts
Solution
  • Automation eliminated reactionary response to fraud
  • Sift Insights empowered review team to make informed decisions
Results
  • Chargeback rate reduced by 77%
  • 3.5x ROI using Sift
Jacob Sanchez

The value we get from the people resources at Sift—talking with us about how to unlock our potential and think about not just fraud prevention, but the overall world of Trust & Safety—is enormous.

Jacob Sanchez, Manager, Risk Operations

Challenge

Growing pains bring a need for change

As Favor Delivery grew, so did the number of chargebacks they were experiencing. Favor Delivery was looking to scale across Texas, but was hindered by the fear of increasing an already high chargeback rate. The number of users creating fraudulent accounts was growing and account takeover (ATO) attempts were becoming more frequent, but Favor Delivery didn’t have a machine learning tool to highlight events quickly. Instead, they were using their internal heuristic system to manually search for fraud, which wasn’t scalable and couldn’t keep up with the volume of incoming orders. They needed a proactive solution that could automate and keep them ahead of fraud—not struggling to keep up with it.

Solution

Automation and a wealth of knowledge

In their search for the perfect fraud solution, Favor Delivery found that other vendors required long implementation times and weren’t the best fit for their startup model compared to Sift. Favor Delivery turned to Sift Payment Protection; the integration took less than two months, and within a month after integration Favor Delivery started to see powerful results.

Favor Delivery’s Account Review Team began utilizing Sift Workflows to manage their fraud logic, auto-accepting most orders and auto-blocking the riskiest. With Sift Insights, the team used the Routes metrics to determine how many orders were hitting a given route and whether that was effective or causing too many false positives. And the Network and Activity features within an order accelerated manual reviews, as the team could determine whether an order shared risky attributes with other fraudulent orders and quickly decide whether they wanted to accept or reject the order.

The Sift Console is very intuitive; my agents know what actions they need to take on an order or an account, and we like how customizable it is in terms of the events we can choose to send to Sift.

Jacob Sanchez, Manager, Risk Operations

Results

Scaling with no signs of slowing

Since implementing Sift Payment Protection, Favor Delivery’s chargeback rate has decreased by 77% and they’ve achieved 3.5x ROI. Thanks to accurate, automated decisions, the Account Review team is saving 300 hours per month in manual review. With their chargeback rate under control, Favor Delivery has scaled throughout the state of Texas and continues to grow steadily. They’re now equipped to prevent fraudsters engaging in account abuse on their platform, and will remain one step ahead of ATO and any other fraud that comes Favor Delivery’s way.

Jacob Sanchez

As we start to face new types of fraud and risk to our business, I’m comfortable knowing that Sift will be right there with us to navigate trends and best practices.

Jacob Sanchez, Manager, Risk Operations

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