Data Visualization
The goal of the Data Visualization group is to inspire and empower our users, Fraud Analysts and Data Scientists, to make accurate and fast decisions, making complex data understandable through insightful, accessible, and beautiful data experiences, while enabling the community to evolve together.
Research Focus:
- Enhance explainability and governance of Machine Learning models
- Complex fraud pattern detection and recognition
- Accessibility in Data Visualization in a business setting
Recent Publications
Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis
Published at NLVIZ, a workshop at IEEE VIS 2024
Related Blog Posts
Building Feedzai’s First Figma Charting Library: A Summer Experience
Find out more about the recent Figma Library of charts created to help Feedzai’s Data Visualization and UX teams in their mockups creation process.
Francisca Calisto
Styling Altair Charts with the feedzai-altair-theme
Find out about Altair themes and a new Python package for Data Visualization with Altair.
João Palmeiro
Connecting the dots: how to see the shape of fraud
At Feedzai, we’re in a constant and ever-evolving fight against financial crime. To be efficient fraud fighters, we leverage large amounts of data. Data scientists use our platform to build machine learning models from historical data, which are then deployed to stop worldwide fraudsters in real time.
Beatriz Malveiro
Building Feedzai’s First Figma Charting Library: A Summer Experience
Find out more about the recent Figma Library of charts created to help Feedzai’s Data Visualization and UX teams in their mockups creation process.
Francisca Calisto
Styling Altair Charts with the feedzai-altair-theme
Find out about Altair themes and a new Python package for Data Visualization with Altair.
João Palmeiro
Connecting the dots: how to see the shape of fraud
At Feedzai, we’re in a constant and ever-evolving fight against financial crime. To be efficient fraud fighters, we leverage large amounts of data. Data scientists use our platform to build machine learning models from historical data, which are then deployed to stop worldwide fraudsters in real time.
Beatriz Malveiro
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