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Jennifer Arnold on Optimizing Financial Crime Detection with Minerva

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Manage episode 424178773 series 2119173
Content provided by Thomas Fox. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Thomas Fox or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Innovation comes in many forms, and compliance professionals need to not only be ready for it but also embrace it. In this episode, Tom Fox visits with Jennifer Arnold, a seasoned anti-money laundering (AML) professional. Jennifer is a co-founder of Minerva, the sponsor of this episode. Minerva is an innovative investigation and screening platform.

Minerva is an invaluable tool for financial investigators, enabling quick and efficient data analysis to support informed decision-making. She discusses Minerva’s capability to search for critical data, such as adverse media and criminal activity, enhancing the investigator’s role through automation and speed. By combining the expertise of skilled investigators with advanced data science, Minerva significantly maximizes the effectiveness of AML investigations in today’s data-rich environment.

We take a deep dive into how Minerva integrates AI into its processes for detecting financial crime. The technology employs simple data aggregation to target relevant data sources, performing entity resolution for a nuanced and accurate view of clients. This approach minimizes false positives, streamlines work for the Financial Intelligence Unit and ensures that information examined is meaningful and precise.

Key Highlights:

  • Introduction to Minerva’s AI Integration
  • Data Aggregation and Intelligence
  • Entity Resolution and Contextual Data
  • Accurate Client Risk Assessment
  • Reducing False Positives

Resources:

Jennifer Arnold on LinkedIn

Minerva

Tom Fox

Instagram

Facebook

YouTube

Twitter

LinkedIn

  continue reading

503 episodes

Artwork
iconShare
 
Manage episode 424178773 series 2119173
Content provided by Thomas Fox. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Thomas Fox or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Innovation comes in many forms, and compliance professionals need to not only be ready for it but also embrace it. In this episode, Tom Fox visits with Jennifer Arnold, a seasoned anti-money laundering (AML) professional. Jennifer is a co-founder of Minerva, the sponsor of this episode. Minerva is an innovative investigation and screening platform.

Minerva is an invaluable tool for financial investigators, enabling quick and efficient data analysis to support informed decision-making. She discusses Minerva’s capability to search for critical data, such as adverse media and criminal activity, enhancing the investigator’s role through automation and speed. By combining the expertise of skilled investigators with advanced data science, Minerva significantly maximizes the effectiveness of AML investigations in today’s data-rich environment.

We take a deep dive into how Minerva integrates AI into its processes for detecting financial crime. The technology employs simple data aggregation to target relevant data sources, performing entity resolution for a nuanced and accurate view of clients. This approach minimizes false positives, streamlines work for the Financial Intelligence Unit and ensures that information examined is meaningful and precise.

Key Highlights:

  • Introduction to Minerva’s AI Integration
  • Data Aggregation and Intelligence
  • Entity Resolution and Contextual Data
  • Accurate Client Risk Assessment
  • Reducing False Positives

Resources:

Jennifer Arnold on LinkedIn

Minerva

Tom Fox

Instagram

Facebook

YouTube

Twitter

LinkedIn

  continue reading

503 episodes

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