Sardine

🎁 Fraud Squad Feature: Card to Name Match. Matching a users card to their name was impossible until now. Matching a user's card to the name they’ve entered can help screen out stolen cards quickly without additional friction. Sardine has this capability, and it’s one we’ve pushed our partners to be able to deliver because we focus on where the data science brings real value to e-commerce companies. As with all fraud rules, combining this check with 100s of others is important.

🤖 Advanced bot detection can save you from huge downstream issues Historically bot detection was left to the Infosec team, while the fraud team looked at payments. In the middle is a chasm of opportunity for conversion optimization and fraud detection. Advanced bots can steal item descriptions and images to create counterfeit pages, spiking chargebacks. Or they might rapidly create new accounts (new account fraud or NAF).

⏪ Pre-auth is a key moment in time Everything that happens before a transaction is a critical signal. If a user copies and pastes their credentials, that could be a key sign of a credential-stuffing attack. Focussing on everything happening here (like bot detection, user device, and behavior) can deliver substantial bang for your buck.

Team Sardine is cooking up something special at the Product x Engineering offsite this week 🥘🥕🥗 Kazuki Nishiura Andrew Austin Krisan Nichani Aditya G. Alex Kushnir Alex Popelard Anton Sidorovich Brendon Matos Ethan Itovitch Izel N. Marcos Schwartz Raj Lad Rebeca Ferreira Soups Ranjan Srikant Rao Suhail Ranger Thomas Todd Zahid Shaikh

🎁 Feature drop: Merchant Risk Scoring. Onboarding merchants as a PSP or marketplace should be automatic. So we automated it with AI ⚡ Standard KYB data pulls are valuable, but often weak signals like ensuring the website matches the business description are super useful in due diligence. We can pull things like 👉 The “TrueIndustry” of a merchant by pulling their state fillings and predict MCC & six-digit NAICS codes 👉 Analyze the professionalism of a website 👉 Provide ongoing monitoring of OFAC lists, and key merchant data with legal or state agencies There are entire categories of companies dedicated to this, and I take my hat off to them, its a much needed product. My challenge as a leader of a fraud and AML team was always making the tools work together. We’ve built this (and 1,000s of other) features to play well with your existing tech stack whether you’re an enterprise or startup.

🤯 Mindblowing stat: Half of all banks have inadequate operational risk says the OCC. Operational risk includes 👉 Cyber attacks and data breaches 👉 Critical Third Party risks 👉 Employee blunders or technology failures We often think about the risks banks present, like bank runs, as we saw with the Silicon Valley Bank collapse. But it's far more likely more frequent failures occur in third-party risk management. Financial institutions need complete oversight and visibility into their 3rd and 4th party relationships. 🐟 Traditional buying and procurement approaches rely on spreadsheets. Often a risk is logged in a spreadsheet, but the response isn't automated. 🐟 In recent guidance, the Federal agencies noted that Fintech companies could help respond to threats in real time and help all FI's deal with a world changing faster than their processes have. 🐟 The solution: Better dashboards, real-time visibility, and full control of 3rd parties through a single or a handful of interfaces. How? Drop us a line and we'll show you some examples 📧

Booking your trip to MRC San Diego? We'll be there to share how we're beating first-party fraud, returns fraud & scams. If you haven't booked yet early bird pricing ends July 23 (AKA, tomorrow!) Here's a handy link 👉 https://lnkd.in/eytNkwRf MRC | Merchant Risk Council

Advanced bot detection can save you from huge downstream issues Historically bot detection was left to the Infosec team, while the fraud team looked at payments; in the middle is a chasm of opportunity for conversion optimization and fraud detection. Advanced bots can steal item descriptions and images to create counterfeit pages, in turn, spiking chargebacks. Or they might rapidly create new accounts (new account fraud or NAF).

User behavior is undefeated for detecting payment fraud. We had one client who reported a single rule that looked into how data is entered auto declined transactions 2x more effectively than any other rule they had setup. That moment in time pre-payment and pre-auth gives so many clues, and so many tells. Those passive signals offer huge value. We’ve pre-built thousands of rules to help our clients quickly switch them on and reap the benefits.

Too many companies still ignore the "pre-auth" stage of a card transaction. This is a huge lost opportunity to increase conversion and reduce fraud. Most fraud checks happen after a user has clicked “buy” and the payment has started. But we can get earlier. Pre-auth signals include 1,000s of possible clues to a user intent like: 👉 Email reputation: Is this email address being aged but mostly dormant, or active? 👉 Expert mouse movement: Is this user creating accounts and getting to checkout a little bit “too fast?” 👉 Device red flags: Is the user hiding their location, and secretly in a high risk jurisdiction? The right signals before auth can screen for good users and bad users. Any high risk users, we can throw in increased friction before starting a payment. The result? Less bad users get as far as the payment and fraud checks. Good users convert faster. 👉 Are you recording the screen? 👉 Are you taking screenshots? These signals weren't available. A big reason I founded Sardine is to deliver them so we can stop more scams. We make them available to all of our customers. It's not just about having lots of signals. We need to have the right ones.