New Show Hacker News story: Launch HN: Dyneti (YC W19) – Helping apps stop fraud and process payments faster
Launch HN: Dyneti (YC W19) – Helping apps stop fraud and process payments faster
57 by julia-zheng | 55 comments on Hacker News.
Hi HN, We’re Julia and Lena, the founders of Dyneti ( 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 ). 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
57 by julia-zheng | 55 comments on Hacker News.
Hi HN, We’re Julia and Lena, the founders of Dyneti ( 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 ). 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
No comments