DataVisor

Today’s digital financial frauds require modern digital fraud solutions. Ones that not only meet the level of technology used by fraudsters but give fraud teams the upper hand. Device intelligence is one of those tools, and it’s a critical part of any dependable fraud solution. There are a few key things device intelligence does that make it indispensable for fraud fighters: 🔍 Device intelligence reveals emulators, bot scripts, botnets, cloners, and manipulated devices. The presence of any of these is a sure sign of a fraudster at work. 🏁 Device intelligence works from the start of the customer journey, helping thwart account creation fraud by revealing synthetic IDs and suspicious accounts. It remains a valuable tool in finding other frauds throughout the customer lifecycle too. ⏱️ Device intelligence can work in real time and adapt to fraud patterns when powered by AI. Leading device intelligence solutions also scale and work flexibly to handle growing volumes of data. Choosing a device intelligence solution comes down to factoring in accuracy and precision, scalability, and integration capabilities among other factors. But the fact remains, fraud teams that don’t already have device intelligence need to prioritize implementing it to stay on par with modern fraud attacks. #ai #artificialintelligence #fraudprevention #machinelearning #deviceintelligence

According to National Credit Union Administration data, the credit union system’s net worth increased by $13.3 billion in just one year. Credit unions are growing at a faster rate than nearly any time in recent history. Just like any FI, more growth comes with more fraud attacks. Here are 5 ways credit unions can grow without increasing fraud. 👉 https://lnkd.in/eq7Akm8J

How do you trust something you can’t see—or even explain? That’s the conundrum facing many users of leading machine learning solutions that use a black box model. But while there is some bias in the space that black box works better, are we sure that’s really the case? DataVisor CEO Yinglian Xie explores that question in her Forbes article and shared her take. 👉 https://lnkd.in/ee_4iSAT #forbesfriday #ai #artificialintelligence #fraudprevention #machinelearning

Many factors come into play when building or selecting a fraud solution. How do you prioritize when it comes to your platform? #ai #artificialintelligence #fraudprevention #machinelearning

Featured Innovator Fang Yu, Co-founder and CPO of DataVisor, Inc., shares insights on the crucial role of implementing necessary friction in customer experience. Learn more from other featured Innovators: https://lnkd.in/ebB8KtWN #CustomerExperience #FraudDetection #AI #Fintech #MitekInnovators

Friendly fraud is a bit of a misnomer. When real customers use their own information to commit fraud, it’s anything but friendly to FIs. But with card disputes projected to jump 40%, it’s clear first-party fraud, or “friendly fraud” isn’t going anywhere anytime soon. FIs, merchants, and BNPL providers alike need to crack down on friendly fraud by using modern, advanced fraud detection tools. 👉 https://lnkd.in/eamyz9ar

DataVisor is empowering financial institutions of all sizes with newly enhanced multi-tenancy capabilities. We are thrilled to announce these significant enhancements that enable core banking providers, processors, and acquirers to offer advanced fraud and AML tools to their customers, along with the flexibility to choose the right level of data segregation, control, and configurability for their business. Read more: https://lnkd.in/eJsP2TCz

📈 The BNPL market is expected to be worth $3.27 trillion by 2030. Yes, TRILLION. That number may cause some sticker shock, but when you think about how often you see BNPL options when shopping online it’s less surprising and more thought-provoking. What are the implications of BNPL, essentially a loan to shoppers, becoming a top payment option online? As with any rising new payment service, fraud is near or at the top of the list. The merchants rarely bear the responsibility of stopping BNPL fraud—that’s a job for the BNPL providers they work with. So, how should they prepare to detect the fraudsters working to take advantage of BNPL? There are a few key places to start: 👉 Harness comprehensive data sources - behavioral biometrics, device intelligence, and device fingerprinting are key 👉 Real-time data orchestration - all that data needs to be orchestrated in real-time for analysis 👉 Customized rules and features - tailored fraud solutions always outperform generic ones 👉 Unsupervised machine learning - to unveil unknown fraud patterns as they happen 👉 Generative AI - GenAI cuts manual work so fraud teams can focus on creating strategies

As fraud attacks and patterns evolve, your fraud solution needs to be ready to adapt and evolve with them. Whether you’re satisfied with your solution or not, that often means benchmarking how well it works and if you could make improvements. So, we’re curious…

Real-time payments are projected to reach $575 billion in just 4 years, according to data from a new PYMTNS study. It hasn’t even been a year since the Fed rolled out their real-time FedNow solution, but already real-time payments are reaching massive levels. Why? Because real-time payments have been customers’ choice for years. Is your FI ready to respond to the real-time fraud that comes with this increase in real-time transfers? #realtime #payments #ai #artificialintelligence #fraudprevention #machinelearning