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
Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis
Published at NLVIZ, a workshop at IEEE VIS 2024
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Published at Transactions on Machine Learning Research (07/2024)
Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis
Published at NLVIZ, a workshop at IEEE VIS 2024
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Published at Transactions on Machine Learning Research (07/2024)
Latest News
Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis paper accepted at NLVIZ a workshop at IEEE VIS 2024
Aequitas Flow: Streamlining Fair ML Experimentation paper was accepted in the journal JMLR
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints paper was accepted in the journal TMLR
RIFF: Inducing Rules for Fraud Detection from Decision Trees paper was accepted at RuleML+RR 2024, the leading international joint conference in the field of rule-based reasoning
Fair-OBNC: Correcting Label Noise for Fairer Datasets paper was accepted at ECAIF 2024
AutiVizuA11y paper now available in the EuroVis’2024 proceedings
Show Me What’s Wrong!: Combining Charts and Text to Guide Data Analysis paper accepted at NLVIZ a workshop at IEEE VIS 2024
Aequitas Flow: Streamlining Fair ML Experimentation paper was accepted in the journal JMLR
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints paper was accepted in the journal TMLR
RIFF: Inducing Rules for Fraud Detection from Decision Trees paper was accepted at RuleML+RR 2024, the leading international joint conference in the field of rule-based reasoning
Fair-OBNC: Correcting Label Noise for Fairer Datasets paper was accepted at ECAIF 2024
AutiVizuA11y paper now available in the EuroVis’2024 proceedings
Recent Blog Posts
Aequitas Flow step-by-step: a Fair ML optimization framework
In this blog post we will visit Aequitas Flow, an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project’s GitHub repository.
Sérgio Jesus
Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics
In the world of financial services, the bank or financial institution’s relationship with the customer relies on digital trust, which is anchored in two fundamental principles. First, it must ensure the person engaging through digital banking channels is genuinely the individual they claim to be. Second, it must confirm that this person is authorized to complete the intended financial transaction.
Javier Liébana
AML Reimagined: LaundroGraph Exploits Graph Structure to Assist Anti-Money Laundering Activities
We introduce LaundroGraph, a self-supervised system based on graph-neural networks to assist experts during anti money laundering
Mário Cardoso
Aequitas Flow step-by-step: a Fair ML optimization framework
In this blog post we will visit Aequitas Flow, an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project’s GitHub repository.
Sérgio Jesus
Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics
In the world of financial services, the bank or financial institution’s relationship with the customer relies on digital trust, which is anchored in two fundamental principles. First, it must ensure the person engaging through digital banking channels is genuinely the individual they claim to be. Second, it must confirm that this person is authorized to complete the intended financial transaction.
Javier Liébana
AML Reimagined: LaundroGraph Exploits Graph Structure to Assist Anti-Money Laundering Activities
We introduce LaundroGraph, a self-supervised system based on graph-neural networks to assist experts during anti money laundering
Mário Cardoso
Research Areas
AI Research
The AI group has a mission of building the next-gen RiskOps AI to safeguard businesses and people from fraud and financial crime that is responsible and explainable by design.
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Data Visualization
The Data Visualization group aims to better elucidate complex data for Fraud Analysts & Data Scientists with insightful beautiful data experiences.
Learn More
Systems Research
The Systems Research group aims to enhance performance & reliability of the RiskOps Platform through innovation in a number of key areas.
Learn More
AI Research
The AI group has a mission of building the next-gen RiskOps AI to safeguard businesses and people from fraud and financial crime that is responsible and explainable by design.
Learn More
Data Visualization
The Data Visualization group aims to better elucidate complex data for Fraud Analysts & Data Scientists with insightful beautiful data experiences.
Learn More
Systems Research
The Systems Research group aims to enhance performance & reliability of the RiskOps Platform through innovation in a number of key areas.
Learn More
Page printed in 14 Sep 2024. Plase see https://research.feedzai.com for the latest version.