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Launch HN: Dyneti (YC W19) – Helping apps stop fraud and process payments faster

60 points by julia-zheng 8y ago

Hi HN, We’re Julia and Lena, the founders of Dyneti (https://dyneti.com https://dyneti.com). Our first product is DyScan, an SDK that helps apps stop fraud and process payments faster by taking a picture of a credit card (https://youtu.be/3gzDECAsqXs https://youtu.be/3gzDECAsqXs). We met about 3 years ago at Uber, where we worked together to fight fraud on the platform. (Merchants are liable for fraud losses on digital transactions). One thing we noticed is a problem industry-wide is that while there is tons of investment in detection (rules and models and features), barely any work goes into figuring out what to do to someone after tagging them as fraudulent. Most of the reliable actions - the ones that actually stop fraud - are very severe (e.g., account banning). In order to minimize good users impacted, fraud systems are built to detect very specific fraud behaviors. It is therefore easy for fraudsters to reverse engineer models and rules and iterate around them, which means even more investment into detection. Along those lines, we noticed few companies realize card scanning is a powerful tool to reduce fraud and improve digital transaction security. Stolen credit card fraud is a major contributor to payment fraud losses. Fraudsters attempting to pay with stolen cards rarely have the physical card on hand, but rather, are running through a list of stolen credit card numbers, expiration dates, and cvvs. Having people enter payment information through a card scan will only allow users with a physical card present to go through with payment. It’s extremely rare to have a tool that both improves customer experience and improves security - but an accurate card scanner accomplishes this. In addition to being a powerful tool for fraud prevention, DyScan also provides a nontrivial conversion boost at checkout by reducing time and effort required to enter payment information (under 5 seconds for DyScan, compared to 21 seconds for manual entry). DyScan is also the only card scanner SDK that works on all credit card formats, including non-embossed numbers, numbers on the back, vertical cards, and Quick-Read format cards (those are the weird ones you may have seen around with a four-digit groups stacked on top of each other). Card.io, which is the card scanner experience you may have seen in other apps, works on only one credit card format (embossed numbers on the front of the card). Other card scanners aren't great because they were constrained technologically at the time they were built. Due to PCI compliance, credit cards must be scanned on device, and it hasn’t been possible to get a good deep learning model small enough to do this until very recently (due to more neural net processing power on devices and better tooling). The additional benefit of this approach is that it means zero latency, which can make a huge difference in terms of user experience and user friction. How it works: After an app integrates DyScan into its checkout process, their users can enter payment information by holding a credit card up to a smartphone camera. At the same time, DyScan verifies that the card is real and non-fraudulent. This results in more good transactions while bad transactions are blocked. We’ve been working hard on DyScan for the past few months and are very excited to share it with the HN community and get your insights on what we’re building. Thanks for reading! Julia & Lena

carlosdp 8y ago
How does it know someone is holding an actual credit card and not a fake printed one?
- julia-zheng 8y ago
  There's a number of ways that a fake card will look different from a real card - we aggregate these signals and form a decision on real vs fake (sorry, we know that's a terrible answer - we would disclose more, but it's best practice to keep specifics of fraud detection a secret to maintain efficacy). Surprisingly, the gap between real and fake is wide enough that we can with good precision separate those cases. Of course, someone could build a replica indistinguishable from a real card, but at that point you've raise the barrier of committing fraud much higher than simply having a stolen credit card number, so chances are the fraudsters would migrate to some other platform
  - ThePhysicist 8y ago
    I once saw a presentation from BSI (Germany Cyber Security Agency) where a researcher used computer vision / AR to create a video feed of a realistically looking ID card based on a simple paper copy of the card. They could add reflections and holograms to the paper copy that looked absolutely realistic, and they were able to use it to pass a video-based identification test (Video-Ident) that's widely used by banks in Germany to remotely validate the identity of new customers. The company then had to change their validation method by asking people to not only hold up and tilt the passport (to reveal the holograms) but to also pass their hand in front of it while holding it, which would lead the AR algorithm to fail. So I'd say it's definitely possible to fool even a person let alone an algorithm, as you said it's questionable though if there aren't any easier ways for criminals to use stolen card numbers.
    - julia-zheng 8y ago
      Thanks for sharing that - super helpful to know. Definitely agree it's possible to make good fake cards, but it makes it difficult enough that fraudsters will usually migrate to a different platform. Since banks are probably the most attractive business to fraudsters, we'd suspect banks would have to make life much more difficult for fraudsters than the average business in order to chase them away.

60 points by julia-zheng 8y ago

carlosdp 8y ago

How does it know someone is holding an actual credit card and not a fake printed one?

julia-zheng 8y ago
There's a number of ways that a fake card will look different from a real card - we aggregate these signals and form a decision on real vs fake (sorry, we know that's a terrible answer - we would disclose more, but it's best practice to keep specifics of fraud detection a secret to maintain efficacy). Surprisingly, the gap between real and fake is wide enough that we can with good precision separate those cases. Of course, someone could build a replica indistinguishable from a real card, but at that point you've raise the barrier of committing fraud much higher than simply having a stolen credit card number, so chances are the fraudsters would migrate to some other platform
- ThePhysicist 8y ago
  I once saw a presentation from BSI (Germany Cyber Security Agency) where a researcher used computer vision / AR to create a video feed of a realistically looking ID card based on a simple paper copy of the card. They could add reflections and holograms to the paper copy that looked absolutely realistic, and they were able to use it to pass a video-based identification test (Video-Ident) that's widely used by banks in Germany to remotely validate the identity of new customers. The company then had to change their validation method by asking people to not only hold up and tilt the passport (to reveal the holograms) but to also pass their hand in front of it while holding it, which would lead the AR algorithm to fail. So I'd say it's definitely possible to fool even a person let alone an algorithm, as you said it's questionable though if there aren't any easier ways for criminals to use stolen card numbers.
  - julia-zheng 8y ago
    Thanks for sharing that - super helpful to know. Definitely agree it's possible to make good fake cards, but it makes it difficult enough that fraudsters will usually migrate to a different platform. Since banks are probably the most attractive business to fraudsters, we'd suspect banks would have to make life much more difficult for fraudsters than the average business in order to chase them away.