Fraud.net, a leading real-time fraud and risk management platform, has partnered with People Data Labs to revolutionize how businesses tackle digital financial crime and fraud risk. By integrating thousands of compliantly sourced datasets into Fraud.net’s AppStore, clients can now access over 3 billion profiles to enhance their platforms, power AI models, and gain deeper insights into their data.
This collaboration allows businesses to better visualize and understand their data, score transactions for risk, and identify fraudulent activity. With the goal of helping companies make more informed decisions, streamline fraud prevention efforts, and mitigate risks, this partnership offers unparalleled data enrichment solutions within a single platform.
Cathy Ross, President and co-founder of Fraud.net, is excited about the potential of this partnership, stating that integrating People Data Labs’ comprehensive dataset into their AppStore will provide clients with enhanced protection against evolving threats. This collaboration aims to empower businesses with the tools and insights needed to combat fraud and manage risk effectively.
Fraud.net’s unified fraudAML and digital risk management platform is designed to help financial services and digital commerce enterprises worldwide reduce fraud, false positives, and financial crime. Through a cloud-born platform, organizations can automate customer onboarding processes, compliance workflows, and make safer, smarter decisions. With a no-code/low-code architecture, natively integrated data and tools from over 50 leading third-party specialists, as well as custom AI models, Fraud.net enables companies to enhance their fraud prevention capabilities and drive profitability.
To learn more about this exciting partnership and the innovative solutions it offers, schedule a meeting with Fraud.net today. Stay ahead of evolving threats and protect your business with the latest in fraud prevention technology.
For more information on fraud prevention and risk management, visit fraud.net.
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