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
Latest News
AutiVizuA11y paper now available in the EuroVis’2024 proceedings
AutoVizA11y EuroVis’2024 presentation available in YouTube channel
Prof. Pedro M. Cruz (Northeastern University) starts part of his sabbatical at Feedzai
3rd edition of Women in Science Scholarships at Técnico now open
DiConStruct paper now available in the CLear2024 proceedings
Two patents granted
AutiVizuA11y paper now available in the EuroVis’2024 proceedings
AutoVizA11y EuroVis’2024 presentation available in YouTube channel
Prof. Pedro M. Cruz (Northeastern University) starts part of his sabbatical at Feedzai
3rd edition of Women in Science Scholarships at Técnico now open
DiConStruct paper now available in the CLear2024 proceedings
Recent Blog Posts
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
TimeSHAP: Explaining recurrent models through sequence perturbations
Recurrent Neural Networks (RNNs) are a family of models used for sequential tasks, such as predicting financial fraud based on customer behavior. These models are very powerful, but their decision processes are opaque and unintelligible to humans and rendering them black boxes to humans. Understanding how RNNs work is imperative to assess whether the model is relying on any spurious correlations or discriminating against certain groups. In this blog post, we provide an overview of TimeSHAP, a novel model-agnostic recurrent explainer developed at Feedzai. TimeSHAP extends the KernelSHAP explainer to recurrent models. You can try TimeSHAP at Feedzai’s Github.
Joao Bento, André Cruz, Pedro Saleiro
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
TimeSHAP: Explaining recurrent models through sequence perturbations
Recurrent Neural Networks (RNNs) are a family of models used for sequential tasks, such as predicting financial fraud based on customer behavior. These models are very powerful, but their decision processes are opaque and unintelligible to humans and rendering them black boxes to humans. Understanding how RNNs work is imperative to assess whether the model is relying on any spurious correlations or discriminating against certain groups. In this blog post, we provide an overview of TimeSHAP, a novel model-agnostic recurrent explainer developed at Feedzai. TimeSHAP extends the KernelSHAP explainer to recurrent models. You can try TimeSHAP at Feedzai’s Github.
Joao Bento, André Cruz, Pedro Saleiro
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 27 Jul 2024. Plase see https://research.feedzai.com for the latest version.