State-of-the-Art Innovations
to Prevent Financial Risk

The Feedzai Research department invests in applied research to improve our products and help users have a better experience. We work closely with Product and Customer Success to develop and transfer innovations. We focus on long-term, disruptive, state-of-the-art research, produce and protect our IP, publish peer reviewed work, contribute to open-source, partner with external researchers, and sponsor scholarships.

Recent Publications

AutoVizuA11y: a Tool to Automate Screen Reader Accessibility in Charts

Diogo Duarte, Rita Costa, Pedro Bizarro, and Carlos Duarte

Published at EuroVis 2024

PDF | GitHub | npm | YouTube

DiConStruct: Causal Concept-based Explanations through Black-Box Distillation

Ricardo Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at CLeaR 2024 - Conference on Causal Learning and Reasoning

arXiv

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani

Published at AAAI-24 - Annual AAAI Conference on Artificial Intelligence

arXiv

FiFAR: a fraud detection dataset for learning to defer

Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at ICAIF23 - Synthetic Data for AI in Finance workshop

arXiv | GitHub

AutoVizuA11y: a Tool to Automate Screen Reader Accessibility in Charts

Diogo Duarte, Rita Costa, Pedro Bizarro, and Carlos Duarte

Published at EuroVis 2024

PDF | GitHub | npm | YouTube

DiConStruct: Causal Concept-based Explanations through Black-Box Distillation

Ricardo Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at CLeaR 2024 - Conference on Causal Learning and Reasoning

arXiv

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani

Published at AAAI-24 - Annual AAAI Conference on Artificial Intelligence

arXiv

FiFAR: a fraud detection dataset for learning to defer

Jean V. Alves, Diogo Leitão, Sérgio Jesus, Marco O. P. Sampaio, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Published at ICAIF23 - Synthetic Data for AI in Finance workshop

arXiv | GitHub

Recent Blog Posts

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