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Leveraging graph-based features to overcome the accuracy barrier in fraud detection machine learning

11 Mai 2022
Advanced Analytics & AI Theatre

Despite spending $1Bn on fraud detection software each year, fraud still costs banks over $50Bn annually, and it’s not going down. Many data scientists are building and tuning machine learning models, but fraud detection rates aren’t improving much. The problem is not the algorithms; it’s the data you put into them. US tier 1 banks report Fraud Detection ML breakthroughs by introducing graph features into their existing fraud detection models. Graph features add brand-new information to your Machine Learning model, leading to a quantum leap in accuracy.

Speakers
Ahlem Mustapha, Pre-Sales Solutions Engineer - TigerGraph

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