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
Aequitas Flow: Streamlining Fair ML Experimentation
Published at Journal of Machine Learning Research
Aequitas Flow: Streamlining Fair ML Experimentation
Published at Journal of Machine Learning Research
Latest News
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs paper was accepted in TMLR
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
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
Recent Blog Posts
“Show Me What’s Wrong!”: Enhancing Fraud Detection Analysis by Combining Charts and Text
Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers.
Beatriz Feliciano
The GANfather: Using Malicious GenAI Agents to Combat Money Laundering
Digital systems have become deeply integrated into many aspects of modern life, particularly within the financial sector. While digital banking simplifies day-to-day operations for clients, it also creates new opportunities for malicious actors to exploit these systems.
Ricardo Ribeiro Pereira
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
“Show Me What’s Wrong!”: Enhancing Fraud Detection Analysis by Combining Charts and Text
Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers.
Beatriz Feliciano
The GANfather: Using Malicious GenAI Agents to Combat Money Laundering
Digital systems have become deeply integrated into many aspects of modern life, particularly within the financial sector. While digital banking simplifies day-to-day operations for clients, it also creates new opportunities for malicious actors to exploit these systems.
Ricardo Ribeiro Pereira
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
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.
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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 8 Dec 2024. Plase see https://research.feedzai.com for the latest version.