Feedzai Research investigates a wide range of topics in Machine Learning, AI Ethics, Systems Research and Data Visualization. Our teams carry out extensive studies to design new methodologies and algorithms, and perform careful data driven experiments to validate their new methods. Below you can find a more detailed description for each group.
The Machine Learning Feedzai Research group is committed to advance state-of-the-art machine learning techniques to improve fraud detection, financial crime prevention, anti-money laundering, and detecting and stopping other types of illicit activity.
Moreover, Feedzai Research aims to disrupt the machine learning workflow itself, developing innovative solutions to automate and assist the processes of sampling, feature engineering, labelling and annotation, training, and offline and online evaluation.
Some of the recent group focus is in Recurrent Neural Networks, Active Learning, Transfer Learning, Graph Representation Learning, GNNs, Bayesian Inference, and Optimization.
The Systems Research group is focused on improving the backend platform, both during training time and during production to reduce hardware costs and time-to-market.
The Systems Research group has been working in new, distributed, stream processing systems, and their quality attributes related to low latency, high throughput, recoverability, checkpointing, scalability, and memory consumption, state management, benchmarking, and more.
The Systems Research group also interfaces with the other Research groups and with the Product team to help bring functionality into the Product.
How can we keep people safe from financial crime and avoid unfair discrimination? Can we always understand, isolate and mitigate biases? Can we enhance trust by providing meaningful explanations? Is the complexity of the system reducing accountability and due process?
The FATE research group is working on new approaches to: detect, isolate and mitigate bias in data sets, ML models, and AI systems; improve explainability and interpretability of ML; enable traceability and governance of the ML workflow; explain the impact of models on fairness and transparency; and enhance ethical and compliant practices.
FATE is also establishing collaborations with top universities, community groups and other companies to raise awareness of the ethical questions on AI and society.
The Data Visualization group addresses the challenges of our main personas – Fraud Analysts and Data Scientists – while they investigate financial crime, or while they analyze datasets or models.
For example, a level-1 Fraud Analyst normally wants to make a decision in less than 10 seconds. Our research helps decide what model explanations to show or how to highlight/hide historical data. On the other hand, Data Scientists might have tens of hours to analyze large datasets. Data Visualization Research helps them make sense of data via data understanding visualizations and helps them analyze models via model performance graphs and bias reports.
The group has several areas of interest, including design systems, grammar of graphics, temporal data, uncertainty, geo-visualization, and more.
We are a team of scientists and engineers with multiple and diverse academic backgrounds, from theoretical physics to economics, mechanical engineering to computational neuroscience, but we all have in common the passion for solving complex and innovative data-driven problems to end financial crime, always with a focus on efficiency, user experience and societal impacts of the solutions we research and develop.
Research Data Science and Engineering positions.
Spend the Summer tackling real-world problems.
Develop your MSc or PhD thesis at Feedzai Research.