DataVisor, the world’s leading AI-powered fraud and risk platform, announced the release of its newest solution to prevent instant payments fraud as the FedNow Service expands instant payments options. DataVisor’s Real-Time Payments Fraud Solution leverages rich account life-cycle signals, advanced machine learning techniques and prebuilt rules that have been tested and proven effective for real-time payments fraud scenarios. By incorporating this solution, financial institutions (FIs) can confidently embrace instant payment technologies facilitated by entities such as The Clearing House and Zelle.
With the U.S. real-time payments market projected to grow at a compound annual rate of 10.12% from 2022 to 2027, new instant payments fraud risks will rise. The instantaneous nature of real-time payments reduces the time for FIs to detect and prevent fraud losses and fraudsters can exploit this speed to quickly transfer and withdraw funds before the transaction is identified as suspicious. DataVisor’s Real-Time Payments Fraud Solution is the only solution proven to proactively respond to new and emerging threats in real time.
This new Real-Time Payments Fraud Solution is developed on DataVisor’s existing fraud and risk platform, which leverages sophisticated machine learning to deliver the best overall fraud detection in a rapidly evolving payments landscape. With an open SaaS orchestration platform that supports easy consolidation and enrichment of data, DataVisor’s solution scales infinitely and enables FIs to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device and behavioral intelligence, powerful decision engine and graph-based investigation tools work together to provide guaranteed performance lift from day one.
“DataVisor provides unparalleled security and peace of mind by protecting instant transactions, allowing financial institutions to confidently adopt real-time payments,” said Yinglian Xie, Co-founder and CEO at DataVisor. “In today’s digitalized world, it is imperative to proactively tackle fraud in real time, as it evolves, and our solution is uniquely positioned to accomplish just that – all while boasting high approval rates and limited customer friction that help boost the customer experience and increase revenue growth.”
- Turn-key, pre-built rulesets to protect against the most common real-time payments fraud scenarios. DataVisor offers more than six pre-configured rulesets, hundreds of sophisticated rules and decision flows, including A2A, C2B, C2M, P2P, B2B, and B2C to cover various real-time payments fraud schemes, including account takeovers (ATO), business email compromise (BEC) scams, social engineering scams, and romance/elderly abuse.
- Proven and tested real-time payments fraud signals. DataVisor’s solution leverages a wide range of data and pre-configured fraud signals that have been extensively tested and proven in the real-time payments domain. These features are dynamically calculated in real-time during production, enabling the system to effectively identify suspicious behaviors such as abnormal velocity and out-of-pattern transactions.
- Best-in-class machine learning. DataVisor’s Real-time Payments machine learning (ML) models include both supervised and unsupervised algorithms. The patented unsupervised solution scales to support more than 10,000 transactions per second (TPS) and less than 200 milliseconds latency. It is the only real-time production-grade solution to capture new, emerging fraud patterns on the fly with higher accuracy rates and lower false positives.
- Real-time alerts and ability to mitigate threats without delays. DataVisor’s real-time intelligence offers stakeholders immediate alerts as fraud patterns change. In minutes, analysts and other non-technical staff can quickly investigate fraud patterns, create new attributes, and deploy new rules on the platform all without using any IT resources.
- Orchestrate different sources of data and real-time step-up authentication. DataVisor orchestrates different data sources, such as call center activities, login activity and dark web data to provide the best decision based on a 360-degree view of each customer. When identifying a suspicious transaction, the platform can also request additional authentication methods, such as SMS in real time, and even put the transaction on hold.
This article was originally sourced from www.ffnews.com