# feedzai Published articles for feedzai. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions DevFeed: [Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions]() Original publisher: [Read original article]() Author: João Bernardo Narciso Published: 2026-04-07T17:24:51Z Content type: article Language: en Sources: [Feedzai]() Topics: [Data visualization](), [data]() Tags: [data](), [data-visualization](), [dataviz](), [decision-making](), [design](), [feedzai](), [fraud](), [fraud-detection](), [patterns](), [research]() ### AI overview Feedzai's Data Visualization Research team is developing Cosmos Explorer, an interface that uses universe-inspired visual metaphors to help analysts examine patterns, trends, outliers, and possible fraud across hundreds of millions or billions of transactions. The project explores how to preserve meaningful details at very large scale while supporting data analysts and data scientists. ### Source excerpt Uncovering the Shape of Fraud with Cosmos Explorer: Visual Metaphors Behind Millions of Transactions The 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/href When we look out into the universe, we don't just see emptiness. We see an unimaginable scale: billions of galaxies, each containing billions of stars, each a point of light carrying its own story. No single observer can take it all in at once. Yet with the right instruments, patterns emerge: the structure of the cosmos itself becomes visible. In the digital realm, there is another universe just as vast and intricate. Every day, hundreds of millions of events flow through Feedzai's system which assesses them to protect consumers all over the world. Each one is a unique data point (e.g., a purchase, a login, a transfer). Individually, they don't tell us much. Together they form a living universe of behavior that represents the diversity in people's lives. But fraud lurks in everyday transactions, with criminals trying to hide their activities within the sheer volume of transactions. The question is: how can we represent those patterns meaningfully, the normal behaviors and the fraudulent behaviors, the trends and the outliers, to empower data analysts and data scientists in their decision-making processes? The biggest challenge is scale. No one can look at billions of events one by one. Aggregation helps, but it smooths over the details, which often encode the most interesting signals like the faint outlines of fraud or unusual clusters of activity. But what if we could see it all at once? Not just a summa ## Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime DevFeed: [Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime]() Original publisher: [Read original article]() Author: Sofia Guerreiro Published: 2025-07-25T11:59:49Z Content type: article Language: en Sources: [Feedzai]() Topics: [Network](), [Artificial Intelligence](), [AI Models]() Tags: [ai](), [ai-models](), [banking](), [feedzai](), [financial](), [financial-fraud](), [fraud](), [fraud-detection](), [genai](), [machine-learning](), [network](), [network-intelligence](), [report](), [research](), [scams]() ### AI overview This blog post introduces Feedzai TrustScore, part of the Feedzai IQ solution, as an AI-based approach to financial fraud detection. It describes how network intelligence and aggregated knowledge from Feedzai's processed events are intended to help detect threats earlier and adapt to changing fraud patterns. ### Source excerpt By Sofia Guerreiro, Ricardo Ribeiro Pereira, Iker Perez, Jacopo Bono Detecting 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 problem is becoming even more challenging. In fact, fraud schemes are spreading faster, growing more sophisticated, and becoming increasingly coordinated across geographies. Meanwhile, many financial institutions still rely on detection systems that are either too rigid (e.g., relying on handcrafted rules) or too narrow (e.g., dependent on custom AI models), and neither option can keep up with the pace of change. At Feedzai, we've developed a new approach. In this blog post, we introduce Feedzai TrustScore, part of the Feedzai IQ™ solution. This is a state-of-the-art AI solution that combines the aggregated knowledge from our network of $8.02T yearly processed events to detect threats earlier, adapt continuously, and safeguard financial systems with speed and precision. Traditional Fraud Detection: Catching a Moving Target With a Fixed Net Every year, millions of people fall victim to financial fraud or scams, with a recent report revealing an average loss of over $2,000 per victim. To protect their customers, banks and other financial institutions have made great efforts towards detecting these suspicious transactions, sometimes temporarily blocking them, conducting internal reviews, and contacting the account owner to validate the activity. However, this is an inherently adversarial task, as malicious actors adapt to whatever detection system the banks have in place. These playe ## The GANfather: Using Malicious GenAI Agents to Combat Money Laundering DevFeed: [The GANfather: Using Malicious GenAI Agents to Combat Money Laundering]() Original publisher: [Read original article]() Author: Ricardo Ribeiro Pereira Published: 2024-10-04T13:49:42Z Content type: article Language: en Sources: [Feedzai]() Topics: [Generative AI](), [synthetic-data](), [Machine learning](), [AI Development](), [data]() Tags: [ai](), [banking](), [data](), [feedzai](), [financial-sector](), [gans](), [genai](), [generative](), [generative-ai](), [legacy](), [machine-learning](), [money-laundering](), [research](), [synthetic](), [synthetic-data](), [transactions]() ### AI overview Feedzai describes a method that uses Generative AI to create synthetic data simulating realistic money-laundering activity. The generated examples are intended to support machine-learning approaches to detection and help identify vulnerabilities in banks' defenses, addressing the limited availability of labeled data. ### Source excerpt 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 Learning models, but these usually require labeled data to train, which are mostly unavailable in this use case. To tackle this problem, we employ advanced Generative AI (GenAI) techniques to generate synthetic data that simulates realistic money laundering activities. These synthetic examples help us identify vulnerabilities and strengthen the defense mechanisms used by banks and other financial institutions In this blog post, we will explore the method developed by Feedzai, which leverages GenAI to tackle the challenges of detecting and preventing money laundering in today's digital landscape. This blog post is the first of a series dedicated to the work done on GenAI by Feedzai Research in the last few years. Problem Statement First, let's briefly introduce the concepts behind money laundering and the difficulties that banks face when trying to prevent it. Money laundering is the process of concealing the origins of illegally obtained funds. Criminals cannot directly spend "dirty" money without risking exposure of their illegal activities. Therefore, they want to disguise the origins of funds before using them. Money laundering typically involves three stages: Placement: the money is introduced into the financial system, often in small amounts spread across various banks. Layering: the money launderer moves the funds through a series of transactions, typically across multiple fin ## Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics DevFeed: [Building Trust in a Digital World: The Role of Machine Learning in Behavioral Biometrics]() Original publisher: [Read original article]() Author: Javier Liébana Published: 2024-06-21T14:01:53Z Content type: article Language: en Sources: [Feedzai]() Topics: [Digital Trust](), [Machine learning](), [Machine Learning, Security Attacks]() Tags: [core](), [digital-trust](), [feedzai](), [fraud-detection](), [fraud-prevention](), [machine-learning](), [online-fraud-prevention](), [research]() ### AI overview This article explains how Feedzai's Digital Trust solution uses behavioral, device, network, operating system, and browser data to support digital identity verification and transaction authorization. It describes a machine learning model that continuously evaluates collected data and uses insights from previous fraud to improve fraud detection and prevention. ### Source excerpt 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 for potential threats, such as malware attacks, and evaluates contextual factors like network, operating system, or browser information to gain a complete understanding of the user's environment. However, the high volume and heterogeneous nature of the collected data, among other challenges, makes detecting potential fraudulent sessions with high accuracy a formidable endeavor. In this blog post we explore how a new machine learning (ML) model that performs a continuous evaluation of collected data and leverages insights from previous frauds to vastly improve Digital Trust's fraud prevention capabilities. We will start with an introduction to technical details behind our Digital Trust solution, going into the challenges of fraud detection and prevention. We continue by explaining how we can apply ML to boost fraud detection and how we deployed the new Fraud model to dozens of Feedzai customers. Table of Contents 1. Digital Trust data collection - 1.1 The user journey - 1.2 Behavioral biometrics data - 1.3 User's behavior - 1.4 Device and network data 2. Challenges to detect fraud in Digital Trust 3. Machine Learning for Digital Trust - 3.1 The holistic approach - 3.2 New Fraud Model 4. Deploying the model 5. In summary Digital Trust data collection To better identify the challenges that are typically faced when designing a fraud prevention system based on Digital Trust, first we