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"Feedzai Intelligence Network (FIN)" Podcast
1 Videos • Playlist
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Anti-Money Laundering
8 Videos • Playlist
Latest
15:03
Finding Nemo: Fishing in Banking Networks Using Network Motifs
Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discover new […]
04:01
GUDIE: A Flexible, User-defined Method to Extract Subgraphs of Interest from Large Graphs
Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in […]
09:54
Machine learning to detect money laundering in Bitcoin blockchain in the presence of label scarcity
Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless […]
15:30
Dense Flows: A Generative Adversarial Method to Train Models to Detect Money Laundering
Money laundering is the process of concealing the origins of illegally obtained money. It is associated with serious crimes, such […]
17:22
Active Learning for Imbalanced Data Under Cold Start – ICAIF’21
Modern systems that rely on Machine Learning (ML) for predictive modeling, may suffer from the cold-start problem: supervised models work […]
11:14
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Authors: Diogo Leitão, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro Human-AI collaboration (HAIC) in decision-making aims to create synergistic […]
04:57
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. […]
08:30
Anti-Money Laundering Alert Optimization Using Machine Learning Graphs
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug […]
09:28
Understanding Unfairness in Fraud Detection Through Model and Data Bias Interactions
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. […]
18:02
Data+Shift: Supporting Visual Investigation of Data Distribution Shifts by Data Scientists
Machine learning on data streams is increasingly present in multiple domains. However, there is often data distribution shift that can […]
22:38
FairGBM: Gradient Boosting with Fairness Constraints (Cutting Edge Machine Learning)
FairGBM is game-changing algorithm for Fair Machine Learning that is open source for non-commercial uses, making it accessible to everyone! […]
04:57
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation (Fair ML)
Fair ML: What tabular dataset should be use when evaluating fair machine learning? Don’t miss this short webinar, and for […]
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