Quantifind Innovation to Enable Proactive Screening and Risk Monitoring
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 doing a reverse search with risk, business, and geographical information.
Automated Risk Discovery Enables Efficient Detection of Unknown Risk
Risk analysis is arduous and inefficient when analysts lack specific names or access to sensitive information on unclassified networks. Inefficiency increases an organization’s vulnerability to threats. Quantifind’s Automated Risk Discovery solution, powered by the feature-rich risk intelligence platform, Graphyte™, allows analysts to assess potential risks in real-time. Instead of entity names, analysts utilize Quantifind’s comprehensive knowledge graph to gather risk-related information and generate relevant entity lists. No other vendor offers this capability.
“Intelligence analysts are tasked with understanding threats specific to regions worldwide 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 related to 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 networks of influence 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 at the click of a button and with real-time application responsiveness. 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 responsible for assessing, reporting, and mitigating risks for 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 out the competition with its strong data science foundation that leads to superior speed and accuracy,” Tier 1 Global Bank.
Aligned with Quantifind’s explainable AI methodology, this web-based solution includes native data provenance features, providing a complete account of all risk and relationship evidence. Full-featured reporting allows seamless integration of Quantifind’s Knowledge Graph information into threat assessments and intelligence reports. The solution is easily accessible through an always-on, immediately available SaaS delivery model.
Quantifind Unveils AI Roadmap to Accelerate Risk Management Innovation
In response to economic conditions and rising global threats, Quantifind has expedited its AI innovation to empower global analysts in combating 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 off of the latest advances in the field, we continue to deliver fast, scalable, cost-effective solutions that can be put into practice today. Now more than ever, our customers need to streamline processes so that they can focus faster.”
Stakeholders can anticipate significant AI advances incorporated into their technology stack, delivering speed, accuracy, and explainability:
- A next-generation Knowledge Graph that utilizes up-to-the-minute data and information, allowing users to assess risk-in-relationship due to connected entities in any risk assessment, on-demand.
- Risk model advancements that significantly expand the use of contextual information for improved accuracy, applied at the scale of millions of unstructured documents per day.
- Expanded large-scale machine translation and transliteration coverage across 100+ languages and scripts.
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