Sift Case Studies How DoorDash is protecting merchants and consumers from fraud

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	How DoorDash is protecting merchants and consumers from fraud Sift

How DoorDash is protecting merchants and consumers from fraud

Sift

Technology Category	Cybersecurity & Privacy - Application Security
Applicable Industries	Food & Beverage Retail
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Fraud Detection
Services	Cybersecurity Services
Challenge	DoorDash, a technology company that enables merchants to reach consumers via delivery, was facing a significant challenge with fraudsters. These fraudsters were using stolen credit cards and reselling DoorDash as a service illegally. They would advertise online through various platforms, claiming to be selling DoorDash at a significant discount and convincing consumers to make purchases through them. This left DoorDash in a position of having to reimburse the victim (either directly or via chargeback) whose credit card was stolen after the victim disputed the charge. DoorDash was also experiencing chargebacks due to the charges on those stolen credit cards, and their rules-based fraud prevention needed to be regularly updated to stave them off, consuming time and resources. In these early days of DoorDash, no automation was in place and most fraud prevention was done via manual review. DoorDash needed a solution that could proactively detect and prevent these fraudsters before they could make it onto the platform to do damage. Read More
About Customer	DoorDash is a technology company that connects people through delivery. It enables merchants to reach consumers via delivery, providing Dashers (delivery people) with the freedom and flexibility to work when they want, while empowering restaurants to reach a greater pool of customers. Operating in the US, Canada, and Australia, DoorDash has both a desktop site and a mobile app, with most traffic coming through the iOS app. However, the company was facing significant challenges with fraudsters who were exploiting the app, using stolen credit cards and reselling DoorDash as a service illegally. Read More
Solution	Without enough manpower or bandwidth to build a fraud prevention tool internally, DoorDash’s Risk team turned to Sift’s Payment Protection product to ensure the integrity of their community. DoorDash chose Sift over other fraud prevention vendors because of the sheer amount of customers Sift works with across a number of industries. Following integration, DoorDash started to see significant, impactful results. Using the Network view, DoorDash’s Risk team can see how many users on the platform are connected, enabling quick identification and removal of colluding fraudsters or fraudsters using multiple accounts. And on the Analyze page, they’re regularly reviewing their fraud-fighting strategy, testing their existing rules, and making new ones by leveraging the analytics available on the page. Via Workflows, the team automated the labeling of fraudulent-looking users who initiate chargebacks (used in tandem with information they receive from their third-party payments processor). The user is prevented from returning to the platform, and the machine learning model recognizes that fraudulent behavior in other users. Read More Log in to view content
Contents

Technology Category

Cybersecurity & Privacy - Application Security

Applicable Industries

Food & Beverage

Retail

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Fraud Detection

Services

Cybersecurity Services

Challenge

DoorDash, a technology company that enables merchants to reach consumers via delivery, was facing a significant challenge with fraudsters. These fraudsters were using stolen credit cards and reselling DoorDash as a service illegally. They would advertise online through various platforms, claiming to be selling DoorDash at a significant discount and convincing consumers to make purchases through them. This left DoorDash in a position of having to reimburse the victim (either directly or via chargeback) whose credit card was stolen after the victim disputed the charge. DoorDash was also experiencing chargebacks due to the charges on those stolen credit cards, and their rules-based fraud prevention needed to be regularly updated to stave them off, consuming time and resources. In these early days of DoorDash, no automation was in place and most fraud prevention was done via manual review. DoorDash needed a solution that could proactively detect and prevent these fraudsters before they could make it onto the platform to do damage.

About Customer

DoorDash is a technology company that connects people through delivery. It enables merchants to reach consumers via delivery, providing Dashers (delivery people) with the freedom and flexibility to work when they want, while empowering restaurants to reach a greater pool of customers. Operating in the US, Canada, and Australia, DoorDash has both a desktop site and a mobile app, with most traffic coming through the iOS app. However, the company was facing significant challenges with fraudsters who were exploiting the app, using stolen credit cards and reselling DoorDash as a service illegally.

Solution

Without enough manpower or bandwidth to build a fraud prevention tool internally, DoorDash’s Risk team turned to Sift’s Payment Protection product to ensure the integrity of their community. DoorDash chose Sift over other fraud prevention vendors because of the sheer amount of customers Sift works with across a number of industries. Following integration, DoorDash started to see significant, impactful results. Using the Network view, DoorDash’s Risk team can see how many users on the platform are connected, enabling quick identification and removal of colluding fraudsters or fraudsters using multiple accounts. And on the Analyze page, they’re regularly reviewing their fraud-fighting strategy, testing their existing rules, and making new ones by leveraging the analytics available on the page. Via Workflows, the team automated the labeling of fraudulent-looking users who initiate chargebacks (used in tandem with information they receive from their third-party payments processor). The user is prevented from returning to the platform, and the machine learning model recognizes that fraudulent behavior in other users.

Impact #1	DoorDash is preventing thousands of dollars a day in fraud losses.
Impact #2	The Risk team is faster and more efficient; without Sift, each person could only review a handful of cases a day, but with Sift they’ve increased their efficiency 2x-3x.
Impact #3	DoorDash is making more informed decisions, thanks to the shared intelligence of the global network, and as a result they’re ensuring their platform is a safe place for Dashers, merchants, and customers.

Impact #1

DoorDash is preventing thousands of dollars a day in fraud losses.

Impact #2

The Risk team is faster and more efficient; without Sift, each person could only review a handful of cases a day, but with Sift they’ve increased their efficiency 2x-3x.

Impact #3

DoorDash is making more informed decisions, thanks to the shared intelligence of the global network, and as a result they’re ensuring their platform is a safe place for Dashers, merchants, and customers.

Benefit #1	Preventing thousands of dollars a day in fraud losses
Benefit #2	Increased review efficiency 2x-3x

Benefit #1

Preventing thousands of dollars a day in fraud losses

Benefit #2

Increased review efficiency 2x-3x

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Overview

How DoorDash is protecting merchants and consumers from fraud

Operational Impact

Quantitative Benefit