PALO ALTO, Calif., Sept. 19, 2022 (GLOBE NEWSWIRE) — In the area of financial crime risk and compliance, investigators rely on big data and expert judgment to label individuals and organizations as clients based on their potential risk. To develop more accurate risk labels and better protect financial institutions, Quantifind developed ‘Risk Maps’, a standard to improve computer models and better solve risk issues.
Quantifind’s Graphyte platform uses artificial intelligence to resolve entities and then tag them with risks. These labels can alert customers to relevant, risk-related information about an individual or organization. Customers can then mitigate the risk that entity poses by proactively protecting their business or mission with the appropriate measures.
Risk exposure threats are diverse and come into play across multiple sectors. Banks primarily focus on financial crime regulations and policies, while government agencies tend to deal with malicious and international actors. Investors, on the other hand, want to ensure financial stability and security. In the real world, these risks are correlated and intertwined. Financial instability can lead to financial crime, which in turn creates national security problems. For example, wildlife trafficking networks correlate and converge with arms trafficking networks, sharing the same infrastructure and people.
However, terminology or “signals” from these different fields and their data sources can lead to AI creating ambiguous risk labels, leading to inconsistent or incorrect results. Definitions of the same risk differ between private and public institutions and are not applied consistently or at the same level of quality. Quantifind’s risk maps are one way to solve this problem by setting standards that support the collective identification and intervention of crime.
Risk maps are used to clearly define individual risks (e.g. corruption, disinformation, etc.), to clarify gray areas (cybercrime vs. war crimes, human trafficking vs. smuggling), to collate important terms, to provide training examples and to share data sets and signals that relate to the risk. These definitions should be able to be interpreted and operationalized by both humans and machines that check companies for these risks. Thus, the cards provide an effective means for non-technical domain experts to inform semi-automated workflows, establish a common language, and “train” both analysts and algorithms.
In addition to the individual risk assessment, a risk map taxonomy also serves as a guide for the effective categorization of risks. A useful strategy for categorizing risks is to assign risks to the social classes in which they pose a threat. Quantifind used one of the oldest taxonomies for class divisions in society, suits in a deck of cards. Diamonds correspond to the trader class, i.e. they are assigned to the “Risk of financial crime” category. Spades refers to a weapon used by the military class, so it’s classified as a “National Security Risk.” Hearts were associated with the clergy class and therefore with health, so this could be classified as “financial health risk”. Finally, clubs relate to the agricultural class and are therefore classified under “ESG risk” which represents environmental-social governance.
“Quantifind’s risk maps – developed from insights and partnerships with government agencies – provide financial institutions with the information, education and insights they need to protect themselves from financial crime,” said CEO Ari Tuchman. “Providing the right tools to protect the financial industry is at the core of what Quantifind and its Graphyte platform aims to provide, and leveraging these risk maps is a critical step forward in the fight against financial crime.”
To listen to Graham Bailey, COO of Quantifind, talk about the key pillar of risk while delving into AI, Name Science and OSINT, watch The Building Blocks of Perpetual KYC podcast here.
For more information on Quantifind’s new risk map sets, visit the Quantifind blog.
Quantifind’s Graphyte AI-powered financial crime automation platform applies deep data coverage, best-in-class risk assessment accuracy, and powerful investigation tools to deliver AML KYC productivity gains of over 40%.
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