Unit21 and Fingerprint Partner to Enhance Fraud Prevention for Fintechs
Enhancing Fraud Detection for Financial Tech Companies through Device Intelligence provided by Fingerprint
Unit21, a global leader in risk and compliance technology, specializing in fraud, money laundering, and financial crime mitigation, has announced a new partnership with Fingerprint. This collaboration aims to deliver real-time signals to detect and stop fraud faster, thereby better protecting customers.
The integration of Fingerprint's device intelligence into Unit21's platform brings a significant boost to fraud and AML prevention. Unit21 now offers fintechs access to persistent, high-accuracy device IDs and a broad range of device-level signals. This drop-in SDK works across web, iOS, Android, and hybrid environments, ensuring seamless adoption without additional development overhead.
One of the key benefits of this partnership is the ability to detect complex fraud types more effectively. For instance, Fingerprint's device intelligence can identify elder and emergency scams by detecting suspicious activity such as new or unrecognized devices accessing an account and IP geolocation mismatches.
The new capabilities can flag potential fraud patterns such as repeated login attempts across multiple user accounts in real time. Fingerprint's device intelligence can also detect tech support scams by identifying the use of virtual machines, developer tools, abnormal device behavior, unusual spikes in activity, and new logins from unfamiliar devices or locations.
In addition, Fingerprint's device intelligence can detect credential stuffing by identifying bot activity and repeated login attempts across multiple accounts from the same device. It can also detect geolocation spoofing by identifying mismatched time zones, use of proxies, and other methods fraudsters use to evade detection.
The partnership between Fingerprint and Unit21 provides a more powerful, flexible, and scalable platform for fraud and AML prevention. Fintechs using Fingerprint and Unit21 can access persistent, highly accurate device IDs and real-time Smart Signals like Bot Detection and VPN Detection.
Unit21's fraud-fighting platform empowers fintechs to build and adapt faster than fraudsters without the need for complex coding, cumbersome reporting processes, or lengthy analyses. The platform collects and analyzes "smart signals" in real time, such as VPN detection, virtual machine detection, and identification of remote access software, delivering actionable insights directly within Unit21’s risk rules engine.
The new capabilities also include AI-powered case acceleration, automated risk scoring and intervention, and network and graph-based analysis. These features help in reducing losses, improving operational efficiency, enhancing detection accuracy, maintaining regulatory confidence, and facilitating collaborative investigations.
In summary, the new device intelligence capabilities powered by Fingerprint represent a significant step forward in fintech fraud prevention. They combine real-time detection, explainable AI, and automated workflows to outpace increasingly sophisticated fraudsters. With this partnership, Unit21 offers fintechs a more robust and efficient solution for fraud and AML prevention.
- The partnership between Unit21 and Fingerprint will utilize technology to enhance cybersecurity in finance by providing real-time signals for fraud detection, aiding in the prevention of financial crimes and money laundering.
- By integrating Fingerprint's device intelligence into its platform, Unit21 now offers fintech businesses a means to detect complex fraud types more effectively, such as elder and emergency scams, tech support scams, and geolocation spoofing.
- The new partnership also allows fintechs to leverage advanced features like AI-powered case acceleration, automated risk scoring and intervention, and network and graph-based analysis, empowering them to improve detection accuracy, reduce losses, maintain regulatory compliance, and facilitate collaborative investigations.
