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.

Everything But the House (EBTH) was facing a challenge with fraudulent bids on their online estate sale platform. Fraudulent activities included users bidding with stolen credit card information or without any real intention to complete their purchase. This not only delayed profits for the sellers but also potentially lowered the selling price of the items when they had to be relisted. The continuous occurrence of fraud could lead to customers questioning the integrity of the site. EBTH was using a tool that sent identifiable information about bidders to its servers, but it was reactionary and didn't offer any proactive notifications. Therefore, the company started looking for solutions that could detect and prevent fraud proactively.

Shippo, a B2B shipping API provider, was facing a significant challenge with fraud. The company's business model, which allows users to create multiple shipping labels before having to pay, made it a target for fraudsters. The majority of the fraud fell into two categories: users who sign up with a fake email address and use a stolen credit card number, and users who create labels, hit the threshold, and then create a new account to avoid paying their invoices. In both cases, Shippo lost money – either from chargebacks from the accounts with stolen credit card numbers or lost revenue from the unpaid invoices. The company needed a solution that could preemptively identify account abuse and prevent users with stolen credentials from purchasing.

As Carousell began to scale, they started to see fraudsters posting fake and spammy product listings for products that either arrived to the buyer not as described or never got delivered to the buyer at all. Carousell didn’t have a way of proactively preventing these listings and relied on user flags to spot and remove them. This meant that these listings not only posed a threat to good users until they were eventually removed but threatened to sully the reputation of the platform, as well. Repeat fraudsters were also finding ways to get back onto the platform even after Carousell deleted their accounts, and continued to post abusive, fake listings with their new accounts. Carousell limits the number of accounts a user may have to a maximum of two, but fraudsters were creating multiple accounts and Carousell was finding it difficult to keep track of them all. Carousell was using a rules-based fraud solution, but it was time-consuming to have to jump in and change rules every time fraudsters changed their tactics.

Traveloka, a leading platform for booking flights and hotels in Southeast Asia, was facing two main types of abuse: payment fraud from stolen credit cards and account takeover (ATO) from stolen credentials and social engineering schemes. Both these problems led to financial loss and, more importantly, damaged user trust and brand reputation. Traveloka had an internal team dedicated to fraud and risk, developing a series of elaborate fraud rules that attempted to provide an automated first screening of all orders. However, as the range of customers on the site changed, Traveloka’s rules-based system couldn’t keep up. They experienced many false positives that were blocking good customers and their orders, leading to poor customer experience. On the ATO side, static rules were missing a lot of cases, weren’t able to adapt quickly enough to emerging trends, and resulted in a lot of false positives, blocking legitimate users from accessing the site.

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.

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.

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.

KSL.com, a user-driven platform of both buyers and sellers, was suffering from an existential problem due to a growing percentage of fraudulent postings. Bad users were scamming legitimate users from all sides: publishing fake listings, taking over legitimate customer accounts, and running scams from hijacked accounts. Malicious users were also harassing the sellers of real listings, trying to scam them out of their goods and services. The main challenge Eric faced was not only finding and eliminating existing fraud, but also blocking bad users as they tried to re-access the site after one device or account was banned. KSL needed the ability to autoban bad users and repeat offenders. Fighting an imposing fraud rate of 75-80% in some of the more popular sections of the site, KSL’s sole fraud analyst wasn’t able to keep up with the demands placed on their internal fraud tools and manual review process.

Patreon’s work connecting fans with creators poses unique challenges, particularly around content, account, and payment fraud. Because their platform relies on the instantaneous transfer of funds – unlike in a traditional e-commerce model where a purchased good can be held while cardholder identity is verified – it is imperative to prevent payment fraud before it occurs. Payment fraud for Patreon comes in the form of either money laundering or traditional credit card fraud – and almost always, there are stolen credit card credentials at play. As their global reach grew, chargebacks and their resulting fees began to rise as well. In addition, manual review of listings and shared content was not keeping up with the rapidly expanding platform community. Patreon, a young and agile company with no fraud prevention measures in place, needed a content abuse and fraud solution that could scale with the business and would ensure the user experience remained seamless.

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.

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.

During the early stages of the company, PayMongo encountered fraud attacks that resulted in financial losses, including an alarming 4% dispute rate. It was crucial for the startup company to prevent this fraudulent activity in order to enable their merchants’ success and scale their own business. In their search for the perfect fraud solution, PayMongo was introduced to Sift at a Y Combinator event and agreed to an assessment. Following the review, PayMongo concluded Sift Payment Protection was an ideal fit for what they were looking for in a fraud tool.

SEOClerks, a marketplace for SEO and other web-related services, was facing a significant challenge with fraud. Their approach to fraud prevention was largely reactionary, with fraudulent accounts being banned after a chargeback was received. However, these users would often return and create new accounts to continue their fraudulent activities. Despite having an IP-based fraud-detection tool, SEOClerks was still experiencing various types of fraudulent activity, including money laundering, referral fraud, account abuse, and friendly fraud. The main issue was money laundering using stolen credit card or PayPal information. They were unable to identify clear relationships between multiple bad users, and their existing fraud tool didn't provide any intelligence for spotting fraud rings or repeat abusers.

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.

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.

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.

Zirtue, a mobile relationship-based lending application, was facing a growing issue of friendly fraud where users were disputing their loan payments falsely claiming they had not authorized the transactions. This was compounded by the fact that Zirtue had access to a very limited amount of user data, preventing them from proactively recognizing suspicious behaviors and stopping the fraud before it happened. Additionally, the vetting process for taking out a loan was lengthy and required tedious and time-consuming email exchanges between Zirtue and the borrower, to ensure the borrower could confirm their identity. This manual work frequently delayed loans, creating headaches for the Data Analytics team and borrowers alike, and it was looking as though another team member would need to be hired to help handle the workload.

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.

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.

Chicago Music Exchange (CME), a leading music equipment retailer, faced a significant challenge with fraudulent orders after switching their website platform provider. They encountered fraudsters placing small to medium-value orders to test the system before moving to higher-value items. Once a fraudulent order got through, it was easy for these cybercriminals to create fraudulent new accounts and multiply their gains. CME had particular difficulty with orders sent to freight forwarding companies, which required an added level of verification to authenticate the transactions and addresses. This meant that CME had to manually contact the customer or research the shipping address, which was time-consuming and not always effective. This was particularly true for more complicated overseas orders, and every time, CME was left to handle the loss.

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.

Coffee Meets Bagel (CMB) is a leading dating application that aims to provide a safe environment for its users to find real relationships. However, the integrity of its community was being compromised by fraudulent users creating fake profiles and engaging in romance scams. These fraudulent activities not only impacted the brand's integrity but also the trust users had in the platform. Fraudsters were sophisticated and quickly adapted to the rules-based systems and methodologies that CMB used to stop them. As the user base of CMB expanded, the company needed a solution that could adapt instantly, stay ahead of fraudsters, and scale as the business grew.

Logitravel, an online travel agency, was facing a significant challenge with fraud as they expanded into new markets. They were vulnerable to various types of fraud, including organized crime fraud rings, friendly fraud in contested ticket sales, and phishing and account takeover. Their existing third-party rules-based fraud solution was unable to keep up with their growing business and evolving markets. After a particularly destructive barrage of fraud, they realized they needed a more robust and scalable solution.

iwantmyname, a global domain name registrar, was facing a significant challenge with fraud. The company was losing 2% of its revenue to fraudulent activities, which was unsustainable given the competitive nature of the industry. The process of detecting fraud was entirely manual, with two of the co-founders checking every single transaction for suspicious signals. This was not only time-consuming but also led to the company blocking all users from countries with high levels of fraud, negatively impacting their business. The company was missing out on revenue from legitimate customers in these countries and existing customers traveling in these countries faced extra hassle with their accounts. The team was spending as much as 30% of their time managing fraud, time that could have been better spent on growing the business and improving customer experience.

Paula’s Choice, a multinational skincare company, was facing persistent fraud patterns on their platform, resulting in an influx of chargebacks. Fraudsters were ordering products in bulk at a discount and then shipping them to other countries to resell through eBay or Amazon for profit. To combat this, Paula’s Choice initially kept a spreadsheet and manually blocked suspicious orders, but soon discovered how challenging it was to manage and stay accurate. They turned to Sift as a solution. However, when they adopted a new payment processor, they switched from Sift Payment Protection to the payment processor’s revenue protection product, which was offered for free. This switch resulted in an immediate inundation with fraud, receiving hundreds of chargebacks—6x their normal volume.

Travelmob, a social marketplace for travellers, was facing a growing trend of fake listings on its site. Bad users were posing as legitimate hosts, posting photos of properties they didn’t own, and trying to con unsuspecting guests into making their payment offsite. This was negatively impacting the customer experience and the company's brand image. Additionally, the company was also dealing with credit card fraud that was resulting in costly chargebacks. Initially, Travelmob began by manually reviewing new listings and booking requests, but this approach was not scalable and fraud was slipping through the cracks. Building dedicated internal tools for fighting fraud would require time and resources that they couldn’t spare, and anything they created internally couldn’t adequately address the complexity of fraud.