Sift

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.

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.

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.

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.

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.

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.

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.