Building trust in AI
decisions
The TRUST Framework is an operational backbone turning Responsible AI into a measurable, actionable reality. By focusing on Transparency, Robustness, Unbiased outcomes, Security, and Testing, the TRUST Framework provides a rigorous standard for evaluating and building AI systems that inspire confidence and ensure long-term sustainability, offering a clear pathway to implement and validate Responsible AI across all parts of the ecosystem.
Recent Publications
Latest News
Ricardo Ribeiro gives a presentation about Transfer Learning at the DSPT meetup in Lisbon.
Ricardo Ribeiro presents our evaluation framework for transfer learning on ECML 2025.
Our AI team presents at the ECML PhD Forum.
Sérgio Jesus defended his PhD thesis at FCUP.
The paper and dataset “SARSum: An Abstractive Summarization Dataset for Suspicious Activity Reports” was accepted in ECAI-2025.
The paper “High Probability Risk Control Under Covariate Shift” was accepted in COPA.
Recent Blog Posts
Here and Now: Reusing Code at Feedzai with JupyterLab Snippets
Data scientists use different Jupyter notebooks every day — ranging from disposable ones for quick tasks to those shareable with clients.
João Palmeiro
“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
Here and Now: Reusing Code at Feedzai with JupyterLab Snippets
Data scientists use different Jupyter notebooks every day — ranging from disposable ones for quick tasks to those shareable with clients.
João Palmeiro
“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
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.
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
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 10 Oct 2025. Plase see https://research.feedzai.com for the latest version.