Machine learning models increasingly support decisions that carry real consequences. For example, they help fraud analysts identify suspicious transactions, assist doctors in assessing patient risk, and inform hiring decisions that can shape people’s careers.As these models become more complex, explainability has become a cornerstone of trustworthy AI. The idea is straightforward: if people understand why a model reached a particular prediction, they should be able to make better, more informed decisions.But there is a fundamental question that often goes unasked: How do we know whether an…
From a Trace to Money FlowHow we built an interactive visualization to uncover money launderingIn a crime scene, any small trace can become evidence, like a string of hair or a fingerprint. But in financial crime, evidence is often fleeting and can go unnoticed. The challenge is, how can you visualize money laundering when the goal is to conceal it?Our answer was to design the Money Flow visualization: a tool to make funds that were once considered invisible into the light.Money laundering is successful when it is untraceable, when the weave is either so dense it’s impossible to parse or the…
Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of TransactionsThe Data Visualization Research team is developing Cosmos Explorer, an interface that leverages universe-related visual metaphors to convey information about the billions of transactions processed by Feedzai. Pedro Cruz, professor at Northeastern University, partnered with Feedzai to bring this idea to life by contributing with his creativity and expertise to solve this challenging visualization problem.https://medium.com/media/1b2ecfadd91d204640462f89fa6ff67f/hrefWhen we look out into the…
Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related TasksWhen we need to visualize and interact with millions, or even just thousands, of individual points while analyzing data, we typically resort to rendering them in the browser using a canvas. The other common approach for the web, SVG, doesn’t scale when the number of individual elements increases to such quantities. However, while solving one problem, canvas charts introduce a new challenge: accessibility.Although SVG charts are not accessible by default, they can be by design. Each part of…
By Jean V. Alves and Ferran Pla FernándezMoving beyond binary classification provides novel insights.In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That’s why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge.Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated “scam…
IntroductionOver the years, we have evolved from using simple, often rule-based algorithms to sophisticated machine learning models. These models are incredibly good at finding patterns in large datasets, but due to their complexity it is frequently challenging for a human to understand why a certain input leads to its respective output. This is especially problematic in areas where high-stakes decisions are being made and where human-AI collaboration is critical.This is why model explainability has gained traction in recent years. The aim of explainability methods is to shed light on what…
By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo BonoDetecting financial fraud is like finding a moving needle in a shifting haystack. Fraud accounts for a tiny fraction of financial transactions, often less than 0.1%. At the same time, fraudsters are constantly adapting their tactics to evade detection. And this happens within a live and dynamic environment, where financial behaviors and technologies are changing over time. In short, this is an exceptionally difficult problem for financial institutions.With the rise of digital banking and new technologies like GenAI, this…
Data scientists use different Jupyter notebooks every day — ranging from disposable ones for quick tasks to those shareable with clients. Over time, more and more notebooks accumulate, making it increasingly difficult to reuse them in whole or in part. To mitigate this problem and make the most relevant pieces of code quickly accessible to every data scientist, we developed JupyterLab Snippets at Feedzai — our take on leveraging code snippets directly on JupyterLab.JupyterLab is a computational notebook platform that enables us to carry on data science work (and beyond) via notebooks. These…
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.However, the impacts of this issue stretch beyond someone’s finances. It can also impact a victim’s life in many dimensions. Detecting and quickly acting upon suspicious transactions is essential to tackle this problem.Finding Fraud Through Data TablesTo review the data of alerted transactions, analysts look at information in tabular format (similar to what is presented…
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. As a result, money laundering has grown particularly prevalent due to this digital expansion.Banks are required to monitor for money laundering activities and issue alerts when suspicious transactions are detected. Typically, monitoring is performed by rules-based legacy systems. A better approach would be to use Machine…
By Sérgio Jesus, Inês Silva, Pedro Saleiro, Hugo Ferreira, Pedro BizarroIn 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.This blog post is based on the paper by Sérgio Jesus, Pedro Saleiro, Inês Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, and Rayid Ghani.Table of Contents:1. What is Aequitas Flow?- 1.1. For Practitioners selecting a model- 1.2.…
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.Addressing these crucial requirements is the core mission of Feedzai’s Digital Trust solution. The solution collects and analyzes comprehensive user behavioral data, scrutinizes device information…
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