The Anti-Money Laundering (AML), Cyber, Trade Surveillance, and Fraud landscape in 2023 will face a significant change in operations due to a perfect storm of events: new US Government regulations, world conflicts accelerating movement and hiding of money, and technological advances, including such as large language models (LLMs), machine learning (ML) and spatial analytics. The new world of ever-more sophisticated fraudsters operating in real-time has created new challenges and opportunities for transforming Financial Crimes programs across industries.
Watch this webinar highlighting why Financial Crimes investigators (AML, Credit Card, Trade Surveillance, and KYC teams) must use new technology to decrease false positives and increase alert-to-case accuracy and effectiveness. Join our panel of speakers from Kinetica and Expero in a discussion about technology approaches such as TimeSeries, geospatial data, generative AI, Machine Learning, Visualization, and Graph Analytics technology trends in 2023. This webinar will provide hands-on demos that illustrate why the status quo is no longer an option to keep your investigation teams ahead of potential gaps in your anti-fraud solutions and government AML legislation.
Key Learning Topics:
Key Challenges In Financial Crimes
Discuss issues in the investigation and SAR processes; emerging threat vectors; impact areas of audit & compliance; and what is to be expected in 2023/24.
Understand why business investigators need to care about new technologies like Kinetica with next-generation GPUs and vectorized CPUs combined with ML/Graph algorithms, master data matching logic, graph analytics, and artificial intelligence such as large language models. These technologies assist in reducing false positives and increasing accuracy.
Empower Investigator Teams
Explore new capabilities in visualization technology and human processes that increase throughput and provide valuable human intelligence, creating quicker and more efficient outcomes for different roles like Fraud Management, Investigators, and Data and Analytics teams.
Rise Of ML & AI With ‘Explainable’ ML
Learn practical methods of harnessing the ensemble of time-series, IoT data, spatial analytics, and ML/Graph algorithms, including LLM/ML, that involve non-technical investigators as ‘humans-in-the-loop’ for higher accuracy and streamlined processes.