AI & Deep Learning in Finance
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Date: Thursday, September 16, 2021
Time: 9:00am – 10:00am PT
Duration: 1 hour

Financial institutions invest a great deal of resources in identifying and preventing financial fraud and money laundering. Due to the ever-evolving complexity of fraudulent activities and the increasing volumes of data, conventional rule-based anti-fraud/money laundering detection systems are overwhelmed. They generate large amounts of alerts, with increasing amounts of false positives and are missing newly emerging schemes due to their technical limitations.

In this webinar, Logical Clocks and NVIDIA will demonstrate how to overcome those limitations by combining regulation-compliant, conventional fraud/anti-money laundering rule-based systems with deep anomaly detection methods--powered by Hopsworks Feature Store, NVIDIA Rapids™, and NVIDIA Triton™ Inference Server. We'll showcase the system’s efficiency in broader low-latency, real-time anomaly detection applications and provide you with step-by-step details on how to develop this state-of-the-art anomaly detection model and manage its entire lifecycle by applying modern MLOps and DataOps principles.

By attending this webinar, you'll learn how to:
  • Develop a state-of-the-art generative adversarial-based anomaly detection model, tune hyperparameters, and train with multiple NVIDIA GPUs in parallel using Maggy on RAPIDS.
  • Create an end-to-end anomaly detection pipeline and manage the model's entire lifecycle by leveraging Hopsworks Feature Store.
  • Perform model serving with NVIDIA Triton Inference Server and use NVIDIA GPU-accelerated network graphs visualization tools to further investigate suspicious transactions flagged by deep anomaly detection models.
Join us after the presentation for a live Q&A session.


By filling out the form, you agree to share your data with our partner, Logical Clocks. Your information will be handled in accordance with Logical Clocks' Privacy Policy.


You will receive an email with instructions on how to join the webinar shortly.

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.


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Davit Bzhalava

Head of Data Science, Logical Clocks

Davit Bzhalava holds a PhD degree in Bioinformatics and is one of the main developers of Hopsworks Feature Store. Prior to Logical Clocks, he held a position at Swedbank as principal data scientist, where he conducted extensive work to build data-intensive platforms and implement efficient anomaly detection frameworks using deep learning techniques.

Prabhu Ramamoorthy, CFA, FRM, CAIA

Financial Ecosystems Partner Manager, NVIDIA

Prabhu Ramamoorthy focuses on HPC/ML/AI acceleration for financial services. Previously, he held leadership roles at margin software firm Dash Regtech and Big Four firm KPMG/EY. Mr. Ramamoorthy holds an MBA from the University of Wisconsin-Madison and an undergraduate engineering degree from BITS-Pilani. He is a CFA charterholder, Financial Risk Manager (FRM), and Chartered Alternative Investment Analyst (CAIA), specializing in financial use cases.

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Webinar: Description here

Date & Time: Wednesday, April 22, 2018