Creative Market, a San Francisco-based design marketplace, has a broad and continually expanding community. Their users vary from big corporate purchasers to independent freelancers, but they all shop through one-click checkout. This simple checkout means less friction, but it also opens the door to payment fraud. As the marketplace grew, payment fraud crept onto the platform. Although the teams originally built an internal rules-based fraud management system to combat the growing threat of fraudsters, their commitment to keeping the marketplace safe meant they wanted to get ahead of fraud and keep their chargeback rate low.

Cozy, a startup that offers a platform to streamline interactions between property managers and renters, was facing a significant challenge with fraud. With listings and renters in 13,000 cities nationwide, the company was experiencing both payment fraud and content abuse in the form of fake rental listings. Fraudsters were using fake rentals to ask for wire deposits from unsuspecting renters. As the company expanded its payment options beyond ACH to credit card, the problem only grew. Cozy needed a solution that could keep up with their wide user base and prevent fraud before it resulted in losses.

Atom Tickets was struggling with chargebacks; their chargeback rate was very high, which decreased their revenue and order volume. There isn’t a team focused on preventing fraud – while there are stakeholders across departments, fraud is a one-man army led by Trust & Safety Specialist Aaron Rennell. Managing all of Atom Tickets’ fraud was already a big job but it got even trickier for Aaron when Atom Tickets experienced spikes in activity during blockbuster movie ticket sales. The significant increase in online movie sales for the company also brought in an uptick in fraudulent purchases for blockbuster must-see movies. Given the increase in potentially fraudulent purposes, they needed a solution that would automate and streamline fraud prevention to make it manageable, and help them greatly reduce their chargeback rate.

GetMyBoat, the world's largest boat rental and charter marketplace, was facing a significant challenge with fraudsters. These cybercriminals were coordinating efforts to exploit the platform for their own financial gain. They would transact in small amounts to test the system and then use associated accounts to complete higher value reservations. The company was also experiencing an increase in chargebacks, which were costing the business resources, time, and money. These chargebacks were in the form of friendly fraud, where customers try to get a refund for a service they already used, and common fraud, through stolen credit card credentials. The company needed a solution to combat these issues and turned to Sift for help.

The luxury apparel and accessories company was experiencing unprecedented growth in both physical locations and e-commerce volume. With this growth came an influx of transactions and a surge of disputes. By early 2015, the company faced a dilemma: should they expand and adjust to manage customer disputes with manual processes, or find an automated dispute management solution to streamline the process? Managing disputes manually would require the creation of a new department, and all of the hiring, training, and onboarding required to furnish said department. With the EMV liability shift deadline growing ever closer, the team knew they couldn’t gain the expertise needed before disputes became an even bigger problem.

When Swan Bitcoin first launched, the platform experienced an influx of fraudulent transfers, chargebacks, and identity reuse that ultimately led to a significant loss rate. The team identified multiple accounts formed under the same identity, spamming SMS with an influx of phone numbers that made it difficult to keep up with. And although implementing an upfront passwordless login system helped reduce fraud rates, the platform was not entirely free and clear of fraud. Swan recognized they needed a more robust fraud prevention solution to stay a step ahead of the sophisticated tactics they were facing. But being in the security-conscious Bitcoin space, Swan also wanted to be sensitive to their customers’ privacy. Because of these concerns, one-time emails and burner phone numbers are more popular with their users—and likely to be key indicators of fraud in many industries. But for Swan, they could simply be the byproduct of cautious but trusted users looking to protect their identities. This made it especially important for Swan to take into account a larger range of signals, and not apply undue friction to every customer.

PartnerStack, a partner relationship management platform, was initially focused on transactional fraud on the end-customer side. However, they soon started to see fraudulent behavior on the partner side as well. They were faced with self-referral abuse, in which sellers tried to impersonate themselves and use their own link to generate revenue, at the expense of PartnerStack. Fraudsters were also attempting to game the rewards system by signing up for as many programs as possible. As these more advanced forms of fraud began popping up, the team recognized they needed a more sophisticated solution. Their original fraud platform was outdated, limited to email address and IP signals, and simply couldn’t keep up with the abuse they were battling. As a publicly-accessible marketplace, it was crucial for PartnerStack to maintain a trustworthy and secure platform—for customers, partners, and their own bottom line.

Remitly, a company that enables customers to transfer money internationally, was facing a significant challenge with fraud. As the company expanded its services in the Philippines, fraud activity increased. The company's risk team, despite having several years of fraud investigator experience, was overwhelmed with the volume of data they had to review. They had set up 80-100 rules to capture suspicious activity, but scaling was proving to be a challenge. The system they were using was also incapable of learning from the company's data.

Touch of Modern, an online destination for curated designer products, was facing an increasing amount of fraud as the company grew. Initially, the company simply absorbed the cost of any chargebacks due to fraud, but this approach was not scalable. The company's customer service team had a very limited window to stop fraud, between processing an order and shipping it. They needed a solution that could accurately stop fraud without blocking legitimate users. The solution also needed to scale with their business, integrate with their internal systems, and automate decision-making to ensure quick processing and fulfillment times.

Zoosk, a leading online dating company, was facing challenges with fraudulent users who were spoiling the experience for legitimate ones. The company was already working to reduce friendly fraud and payments fraud on the site. However, the real-time nature of Zoosk’s service and their ever-expanding user base meant that the company needed a solution that could adapt instantly and scale as their business grew. The existing tools and processes used by the dedicated team tasked with tackling fraud for Zoosk were not sufficient to meet these needs.

Purse, a San Francisco-based company, is working to create the world’s largest online marketplace using bitcoin. They offer the lowest prices and fast, frictionless commerce, processing millions in bitcoins every month. However, they faced a unique challenge with payment fraud. Since bitcoin transactions are final and irreversible, Purse had to detect and remove malicious actors attempting to game their bitcoin escrow system by purchasing items for Purse shoppers with fraudulent/hacked Amazon accounts. Initially, they combated fraud with internal tools, requiring three full-time support staff committed to fraud management and review. However, this was unscalable as the site grew.

Wanelo, a global marketplace for Gen Z, was facing a significant challenge with spammers and scammers. As the company evolved from a social shopping site to a marketplace, it started experiencing payments fraud. The fraud showed up in the form of disputes, with both friendly and scammy customers demanding 'charge not authorized' chargebacks. Nearly 70% of their chargebacks could be attributed to friendly fraud, which was a unique challenge to address because such customers often look like good and valuable users – until they decide that they don’t want to pay. Wanelo’s job then was to convince the bank that the customer is committing chargeback fraud. As more fraudsters attempted bad activity and Wanelo’s chargeback rate crept up to 0.87% – including friendly fraud – Courtney Bode, Marketplace Operations Manager, turned to the system that had worked so effectively for the social side of the company.

A few months after launching their business, Harry’s started considering the need for a proactive, scalable solution to put into place before fraud became a larger problem. Being a trustworthy site is critical to Harry’s business model and core beliefs, so getting the jump on fraud before it became a deep-rooted issue was essential. Harry’s did see some fraud, mainly in the forms of promo abuse, payment abuse, account abuse, and friendly fraud. Resellers would make fake accounts to buy large quantities of blades and sell them them at a profit online, while other fraudsters would use Harry’s for stolen credit card testing. Some returning customers would try to game the system by canceling their subscription after having received product. Harry’s needed a solution that wouldn’t just stop all of these types of fraud, but would teach their fraud team about the tactics of bad users.

ChowNow, an online ordering platform for restaurants, was facing a growing problem of fraud. As the business expanded, so did the losses from chargebacks, which had grown to 1% of revenue. Fraudsters were actively sharing tips on how to defraud ChowNow, and certain locations like Miami, Atlanta, New York, and Chicago emerged as significant sources of fraud. This situation was discouraging growth in large markets. The company was spending an increasing amount of time fighting chargebacks, and was considering hiring a full-time team to tackle the problem. As a growing business, ChowNow didn’t want to restrict sales or spend too much on fraud operations, but couldn’t continue operating with growing revenue loss.

Couchsurfing, a global travel marketplace, was facing challenges with fraudulent content and repeat offenders on their platform. The company operates on a currency of trust, and fake accounts and spammy content were affecting the experience for legitimate users. These issues could involve phishing, malicious comments, or further risk when linked to offline schemes. Couchsurfing had built in-house tools to combat these issues, but they were clunky, led to lots of manual review, and became outdated quickly. They needed a solution that could address a variety of bad content, malicious users, and returning users who were hiding their identity after being banned. Furthermore, there was no way to proactively improve safety for the community by preventing these bad users from creating fraudulent content.

Skillshare, an online learning community, was facing issues with fraud, fake accounts, and spam. Teachers were creating fake student accounts and watching their own classes to increase their earnings. The company also discovered collusion between teachers and students, with fraudsters using stolen credit cards to create many fake student accounts, and then redeeming the same teacher referral code across those accounts to get the fraudulent teacher referral bonuses. Fraudsters were also using Skillshare to engage in SEO spam by creating landing pages on the platform for products they were selling. This was risky as the landing pages could take users off-platform to questionable sites, and it was also detrimental to Skillshare’s reputation. Skillshare’s fraud management was primarily via SQL queries, and these schemes were only discovered after they had already happened. They needed a way to proactively detect and remove networks of colluding users, and to keep SEO spammers off of the platform.

Rently, a company that simplifies the process of finding and showing new homes for both renters and property managers, was facing a significant challenge with fraud. Fraudsters were using fake IDs and selfies found on the internet to bypass Rently’s identification verification process. The company also started experiencing account takeover (ATO); fraudsters were taking over property manager accounts to change property pricing and availability, and provide access to the unit to unauthorized individuals. Without automation, catching ATO in real time during manual reviews proved incredibly difficult. They had a rules-based system in place that was catching some fraud, but wasn’t able to keep up with fraudsters getting smarter and finding ways around those rules to get onto the platform. Rently needed a solution that could automate decisions and proactively detect fraud, rather than continue to react to fraud after an incident had occurred.

Turo, a car sharing marketplace, implemented Sift in 2013 for payment protection. As they grew, they saw account takeover (ATO) becoming a widespread, industry-agnostic issue and took proactive measures to maintain the security of their community. To prevent ATO, Turo required users to verify their identity when logging into an aged account, including resetting the account password. This process created serious friction for the user and a lot of time spent on manual password resets and identity verification for the fraud team. The team was also manually reviewing every user to stave off ATO, labeling thousands of events every day in order to stay on top of their increase in activity. It took 100+ hours of manual operations a week to effectively defend against ATO attempts. Turo needed a way to introduce friction for malicious users, maintain a seamless experience for good users, and spend less time managing fraud.

CoinJar, a well-established digital currency exchange, was facing challenges with identity fraud, chargebacks, and account takeover. Being an online-only platform, it was crucial for CoinJar to have an effective online identity assurance program. The irreversible nature of crypto transactions and the anonymity provided by digital currencies made it a prime target for fraudsters. The company needed a solution that could adapt in real-time and provide effective fraud prevention.

The major American airline was facing a high volume of Visa disputes, which were becoming valid chargebacks. Over the first three months, the company had over 4,700 Visa disputes initiated against them, representing $1.8 million. This was causing significant financial loss and operational challenges for the airline. The company needed a solution to reduce the number of disputes and prevent them from becoming valid chargebacks.

When Studypool first launched, the platform saw users who were taking advantage of tutors by posting questions and later filing chargebacks, in an attempt to get free study help. Some users also tried to game the system by creating fake student accounts so they could pay themselves and later file a chargeback, ultimately getting their money back and a payout from Studypool. At the time, their internal fraud prevention tools couldn’t keep up with the types of fraud surfacing on the platform. The tools were only able to track IP addresses and weren’t accurate or reliable, so Studypool decided to look for a better solution.