Quantifind launches AI-powered risk detection for potential threats

Quantifind, a provider of AI-powered risk intelligence automation to the world’s leading organizations, announced the launch of its latest AI technology designed to enable organizations to proactively screen for potential threats by performing a reverse search using risk, business intelligence and geographic information.

Automated risk detection enables efficient detection of unknown risks

Risk analysis is difficult and ineffective when analysts lack specific names or access to sensitive information on unclassified networks. Inefficiencies increase an organization’s vulnerability to threats. Powered by the feature-rich risk intelligence platform, Graphyte™, Quantifind’s Automated Risk Discovery solution allows analysts to assess potential risks in real time. Instead of entity names, analysts use Quantifind’s extensive knowledge graph to aggregate risk-related information and generate relevant entity lists. No other provider offers this option.

“Intelligence analysts are tasked with understanding threats specific to regions around the world while processing an overwhelmingly large and complex information environment. GraphyteDiscover is an important new technology that aggregates and makes sense of open source data, allowing analysts to assess the key entities and relationships associated with specific threats, regions and other strategic concepts in their entirety.” Craig Dudley, Ph.D., Division Chief, Department of Defense.

Using multiple risk identifiers and a comprehensive filtering mechanism, Quantifind Automated Risk Discovery, delivered through the GraphyteDiscover application, aggregates relevant data points into an elegant, interactive graph that maps influence networks across millions of connected profiles and layers of global data. Analysts can extract local regions of the graph aligned with specific geographic locations, threats, industries, and more—all with the click of a button and with real-time application response. Analysts end up with an accurate list of high-risk entities and their connections.

Automated Risk Discovery serves not only national security analysts, but also financial crime analysts who are responsible for assessing, reporting and mitigating risks to financial institutions. These analysts can also leverage this solution to stay ahead of risks and assess exposure to national and international threats.

“We tested several vendors to improve our AML/KYC processes with external data. Quantifind beat the competition with its strong data science foundation leading to superior speed and accuracy,” Tier 1 Global Bank.

Aligned with Quantifind’s explainable AI methodology, this web-based solution includes native data sourcing capabilities and provides a complete view of all risk and relationship evidence. Full-featured reporting enables seamless integration of Quantifind’s knowledge graph information into threat assessments and intelligence reports. The solution is readily available through an always-on, immediately available SaaS delivery model.

Quantifind reveals AI roadmap to accelerate innovation in risk management

In response to economic conditions and growing global threats, Quantifind has accelerated its AI innovation to empower global analysts to combat risk.

“Quantifind continues to push the envelope when it comes to the real-world application of AI,” says Adam Mulliken, Chief Product Officer at Quantifind. “While drawing from the latest advances in the field, we continue to deliver fast, scalable and cost-effective solutions that can be implemented today. Now, more than ever, our customers need to streamline their processes so they can focus faster.”

Stakeholders can anticipate significant AI advancements incorporated into their technology stack, delivering speed, accuracy and explanation:

• A next-generation knowledge graph using up-to-date data and information, which allows users to assess risk-in-relationships due to connected devices in any risk assessment, on demand.
• Risk modeling advances that significantly extend the use of contextual information for improved accuracy, used at the scale of millions of unstructured documents per day.
• Expanded large-scale machine translation and transliteration coverage across 100+ languages ​​and scripts.