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

Fair-OBNC: Correcting Label Noise for Fairer Datasets

Inês Oliveira e Silva, Sérgio Jesus, Hugo Ferreira, Pedro Saleiro, Inês Sousa, Pedro Bizarro, Carlos Soares

Published at ECAI 2024

arXiv

RIFF: Inducing Rules for Fraud Detection from Decision Trees

João Lucas Martins, João Bravo, Ana Sofia Gomes, Carlos Soares, Pedro Bizarro

Published at RuleML+RR 2024

arXiv | YouTube

Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis

Beatriz Feliciano, Rita Costa, Jean Alves, Javier Liebana, Diogo Duarte, Pedro Bizarro

Published at NLVIZ, a workshop at IEEE VIS 2024

PDF | arXiv

Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints

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

Published at Transactions on Machine Learning Research (07/2024)

arXiv | GitHub | PDF | YouTube

Fair-OBNC: Correcting Label Noise for Fairer Datasets

Inês Oliveira e Silva, Sérgio Jesus, Hugo Ferreira, Pedro Saleiro, Inês Sousa, Pedro Bizarro, Carlos Soares

Published at ECAI 2024

arXiv

RIFF: Inducing Rules for Fraud Detection from Decision Trees

João Lucas Martins, João Bravo, Ana Sofia Gomes, Carlos Soares, Pedro Bizarro

Published at RuleML+RR 2024

arXiv | YouTube

Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis

Beatriz Feliciano, Rita Costa, Jean Alves, Javier Liebana, Diogo Duarte, Pedro Bizarro

Published at NLVIZ, a workshop at IEEE VIS 2024

PDF | arXiv

Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints

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

Published at Transactions on Machine Learning Research (07/2024)

arXiv | GitHub | PDF | YouTube

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