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StackCommerce, a leading native commerce platform, was dealing with a significant amount of fraud involving purchases made using stolen credit cards. The most impactful type of fraud was the loss of digital goods that are distributed instantly. This not only hurt cardholders but also the merchants. StackCommerce needed to stop these transactions as quickly as possible and sought a solution that could prevent them in the first place. They were using a legacy, rules-based solution that didn’t include any machine learning. As the company’s order volume grew, they discovered the shortcomings of rules-based systems: they don’t learn and they don’t scale. The team found themselves reviewing hundreds – or even thousands – of orders per day, and fraud review became unmanageable.

As dbrand's business grew, so did the number of fraudsters creeping onto their site. The majority of the fraud they experienced was from bad users purchasing goods using stolen credit cards. The resulting chargebacks were costly, not only due to the high-quality product that was lost, the sale that was refunded, or the bank-levied chargeback fees, but also the hours of manual review and headaches that the fraud caused. Even as their chargeback rate reached a high of 2.18% in a single month and 4 customer service employees became dedicated fraud management experts, fraudsters continued to slip past their defenses. To mitigate the impact of fraud on their bottom line and brand, dbrand sought a smarter and more scalable solution.

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.

Destinia, a Spain-based online travel agency, faced challenges with payment fraud, fraud rings, and occasional friendly fraud due to the global nature of its offerings. The quick access to flights, hotels, and other digital bookings made manual review unscalable, as the team had a narrow window to investigate hundreds of suspicious orders daily. When chargebacks did hit, it often took over two months for the fees to appear in Destinia’s books, affecting analytics. To prevent chargebacks, rather than simply respond to them, Destinia felt that it was necessary to invest in a solution that required less hands-on maintenance and increased the team’s efficiency.

Etix, the largest independent ticketing company in North America, was facing a growing problem of fraudulent transactions as their online and mobile business scaled. These fraudulent transactions resulted in chargebacks, costing the company money and the invaluable time of fraud analysts who had to respond to fraud attempts. The challenge of discovering fraud through manual review was daunting and unsustainable. Chargebacks often were not reported until after events, making it even more difficult to track and prevent fraud. Etix needed a solution that could respond in real time to potential fraud and prevent fraudulent orders before they were processed.

GetYourGuide, an online platform that connects travelers to experiences, was facing a significant increase in fraud as the range of attractions offered on the site and the number of daily transactions grew. Chargebacks from card-not-present fraud and fraudsters using last minute bookings for nonrefundable products began to impact GetYourGuide’s bottom line. To combat fraud, GetYourGuide’s lean team started manually reviewing suspicious transactions. But this cumbersome process did little to reduce their fraud, and chargebacks remained debilitatingly common. With GetYourGuide, customers had the ability to purchase tickets minutes before walking into an event; this miniscule window of time made scalable and efficient manual review impossible. Even worse, GetYourGuide’s imprecise system for flagging and blocking suspicious transactions produced a high false positive rate. Honest users, frustrated and inconvenienced by slow service, began to complain.

Curve, a company that offers a smart bank card that combines all your cards in one, was facing a growing threat of fraud due to its rapidly expanding customer base. The ability to quickly add new cards with the Curve app and then use them within moments was of particular interest to malicious users who tried to circumvent Curve’s many layers of account authentication. This resulted in expensive manual resources for fraud reviews and chargeback management. As the business was growing at a fast pace, Curve needed a solution to preemptively knock out the growing threat of fraud.

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.

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.

Universe.com, a global events marketplace, was experiencing an increase in fraudulent listings and spambot attacks as it expanded to over 3 million active users worldwide. The volume of event listings, hosts, and users was increasing rapidly, escalating the risks and potential impact of spam, scams, and other fraudulent activity. The attacks were becoming more sophisticated and harder to address. A major client's event was attacked, which was a pivotal moment for Universe. They didn't want customers to hesitate to host major events for fear of attacks. The fraud team was constantly playing catch up. If a fraudulent event was posted or a spambot started messaging thousands of users, the team would only find out about it once it was too late. Each attack meant multiple developers would have to drop what they were working on to address the issue in time.

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.

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.

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.

As Favor Delivery expanded, they experienced an increase in the number of chargebacks. The growth of fraudulent accounts and account takeover (ATO) attempts were becoming more frequent. Favor Delivery was 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.

HelloFresh, the world’s leading meal kit company, faced a significant challenge with users exploiting their promotional offers, which was hurting their bottom line. The company initially tried to tackle these challenges internally through manual review processes in spreadsheets, but quickly found that they didn’t have the breadth of data they needed to effectively detect which customers were exploiting their system. The team decided it was crucial to seek out a more effective and efficient solution on the market instead of building their own capabilities. They were looking for a flexible model that could adapt to each of their market’s unique needs, responsive and knowledgeable customer support, and an adjustable pricing model.

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.

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 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.

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.

Taptap Send, a global remittance service, was facing an increase in fraudulent payments made with stolen credit cards as their business grew. They needed a streamlined fraud prevention solution that was nimble enough to scale across international lines and quick enough to meet customers’ needs. The market for global remittances, which accounts for over $500 billion annually, is dominated by traditional services that are expensive, can take days to arrive, and have limited reach in rural areas. Taptap Send was committed to providing their customers with a speedy, secure, and hassle-free user experience.

Uphold, a multi-asset digital money platform, was facing challenges in ensuring the trustworthiness of new users while reducing friction throughout the customer journey. The company needed accurate risk assessments of the actions taken on their site. This meant deploying additional friction points and manual review before Uphold would allow a customer to transact. Uphold needed a fraud prevention solution that highlighted the riskiness of every action taken on their site and that simplified the review process for their fraud analysts, allowing them to quickly identify linked fraud behaviors between accounts so they could stop fraud fast.

Qube Money, a banking and budgeting app, was facing issues with identity theft and account takeover fraud. Fraudsters were stealing identities and setting up accounts on Qube. International transactions also posed a risk due to more complicated chargeback processes. In the early stages of the startup, the app experienced a fraudulent attack by a fraud ring, costing the company tens of thousands of dollars. As an early-stage startup, they knew they couldn’t afford to have more fraud like this happen on their app.

Banxa, a fast-growing public payments and compliance infrastructure provider for the digital asset industry, faced a significant challenge when its business volume increased by 30x. The company encountered multiple fraud scenarios, including fake profile creation, card fraud, scams, and chargebacks. Initially, Banxa had set up their own fraud function from scratch, handling everything manually when volumes were manageable. However, as Banxa began to grow, this basic model became too limited for their needs. It introduced unwanted friction for trusted customers and became riskier when incorporating multiple variables and increased velocity. So when Banxa’s volume spiked 30x, their fraud rate rose alongside it. The team knew they needed to implement something quickly to support their scaling business, which is where Sift came in.

The email marketing platform was facing a significant challenge due to the susceptibility of the marketing technology industry to fraud attacks. The scale and severity of spam and scams were increasing, putting the onus on sending providers to protect the health of their network. As the company began to scale their business faster than their manual vetting processes would allow, they needed a solution that could keep up. They were looking for a solution that offered uptime, affordability at scale, model customization, data sharing and app integrations, and the ability to automate common support tasks such as account disablement.

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.

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.