Financial Institutions Are Using AI-Created QuantaVerse Reports to Streamline High-Risk Entity Reviews
Citing a surge in client demand, QuantaVerse, which uses AI and machine learning to automate financial crime identification and investigations, has enhanced its entity-based Financial Crime Investigation Report, or FCIR, to assist financial institutions in efficiently conducting periodic entity risk reviews.
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Federal bank regulatory agencies require that financial institutions regularly review and segment all customers based on risk. For example, customers deemed risky by a financial institution are to be reviewed at least once each year. Historically, human investigators manually search through multiple databases and the Internet to determine customer risk, but this process is time-consuming, costly, and inconsistent.
The QuantaVerse High-Risk Entity Report documents risk discovered by the QuantaVerse AI Financial Crime Platform. The platform automates the research work required to assess entity risk, such as adverse media, jurisdiction, transactional relationships, typologies that indicate potential money laundering, and more. By reviewing the completed report, investigators can assess risk more quickly and evaluate more customers than ever before. This QuantaVerse offering also helps segment customers according to a bank’s risk appetite (for example: very high, high, medium, and standard) and provides risk scores (0-100) calculated by the QuantaVerse machine learning engine.
“The High-Risk Entity Report makes the review process more efficient and provides better insights to financial institutions as they conduct on-going entity risk reviews,” explained David McLaughlin, CEO and Founder of QuantaVerse. “Customers using our High-Risk Entity Report can expect to more than double the efficiency of their teams while getting more complete and consistent analysis with fewer errors.”
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