AI-Powered Risk Discovery For Potential Threats Is Now Available From Quantifind

A reverse search using risk, business, and geographic information will enable organisations to proactively screen for potential threats, according to Quantifind, a provider of AI-powered risk intelligence automation to the world’s leading organisations.

Efficient Detection of Unknown Risk is Made Possible by Automated Risk Discovery

When analysts are unable to access individual identities or sensitive data on unclassified networks, risk analysis is laborious and ineffective. The susceptibility of an organisation to danger rises with inefficiency. Analysts may evaluate potential risks in real-time using Quantifind’s Automated Risk Discovery solution, which is powered by the feature-rich risk intelligence platform GraphyteTM. Analysts collect risk-related data and create pertinent entity lists using Quantifind’s extensive knowledge graph rather than entity names. No other vendor provides this function.

“Intelligence analysts are entrusted with digesting an extremely vast and complicated information environment while identifying dangers specific to places throughout the globe. GraphyteDiscover is a significant new tool that compiles and makes sense of open source data, enabling analysts to fully evaluate the significant entities and relationships associated with particular threats, geographical areas, and other strategic concepts. Department of Defence Division Chief Craig Dudley, Ph.D.

Quantifind Automated Risk Discovery, delivered through the GraphyteDiscover application, aggregates pertinent data points into a beautiful, interactive graph that maps networks of influence across millions of connected profiles and layers of global data using multiple risk identifiers and a thorough filtering mechanism. With the touch of a mouse and real-time application responsiveness, analysts can extract small sections of the graph aligned with certain geographic locations, threats, industries, and more. The list of high-risk entities and their links provided to analysts is accurate.

Financial crime analysts who evaluate, report, and mitigate risks for financial institutions are also served by automated risk discovery, in addition to analysts of national security. These analysts might make use of this technology to monitor risks and gauge their exposure to regional and global dangers.

“To enhance our AML/KYC processes with external data, we tried a number of providers. With a solid data science foundation that results in superior speed and accuracy, Quantifind outperformed the competition, according to Tier 1 Global Bank.

This web-based solution has native data provenance elements in line with Quantifind’s explainable AI methodology, giving a full account of all risk and relationship evidence. Quantifind’s Knowledge Graph data can be seamlessly included into threat assessments and intelligence reports thanks to full-featured reporting. An always-on, immediately accessible SaaS delivery approach makes the solution simple to obtain.

Quantifind Announces an AI Roadmap to Speed Up Innovation in Risk Management

Quantifind has accelerated its AI innovation in response to the state of the economy and the escalating challenges facing the world in order to assist global analysts in managing risk.

According to Adam Mulliken, Chief Product Officer at Quantifind, “Quantifind continues to push the envelope when it comes to the real-world application of AI.” “While using the most recent developments in the industry, we continue to create quick, scalable, and affordable solutions that may be used right now. Our clients require process simplification now more than ever before so they can concentrate more quickly.

The technology stack of stakeholders may expect major AI advancements that will bring speed, accuracy, and explainability:

Using cutting-edge data and information, a next-generation knowledge graph enables users to analyse risk-in-relation due to connected entities in any risk assessment on demand.
New developments in risk models considerably increase the use of contextual data for better accuracy and apply to millions of unstructured documents every day.
a larger area of large-scale machine translation and transliteration over more than 100 languages and scripts.