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Join this seminar recording where Erik Altman, a research scientist at IBM, discusses the development and applications of IBM Synthetic Data Sets (SDS) for the financial industry. Learn how these synthetic datasets can effectively label fraud and criminal activity, providing a robust foundation for training AI detection models. Discover why IBM chose an agent-based virtual world design approach rather than using large language models to generate their synthetic data. Altman explains the significant advantages of this methodology while also addressing the challenges it presents when training effective detection models. This 72-minute talk was recorded at Stanford University on May 7, 2025, as part of their HAI Seminar series.
