# Digital Trust Published articles for Digital Trust. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What is Trust and Safety? DevFeed: [What is Trust and Safety?]() Original publisher: [Read original article]() Author: Ben Price Published: 2026-08-28T23:00:00Z Content type: article Language: en Sources: [Sift Science]() Topics: [Security, Privacy and Abuse Prevention](), [Transactions](), [account takeover]() Tags: [account-takeover](), [authentication](), [chargebacks](), [digital-platforms](), [digital-trust](), [digital-trust-safety](), [fraud-detection](), [fraud-prevention](), [payment-fraud](), [trust](), [trust-and-safety](), [trust-safety]() ### AI overview This article explains Trust and Safety as an organizational capability for protecting digital platforms, users, transactions, and interactions. It describes how the discipline brings together fraud prevention, content moderation, abuse prevention, and policy enforcement across the user journey, including payment fraud, fake account creation, account takeover, and content integrity. ### Source excerpt Trust and Safety is the practice of protecting the integrity of digital platforms, their users, and the transactions and interactions that take place on them. As a discipline, trust and safety spans fraud prevention, content moderation, abuse prevention, and policy enforcement across the full user journey. For fraud teams and platform operators, it represents a [...] The post What is Trust and Safety? appeared first on Sift. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity](), [data]() Tags: [analysis](), [comparison](), [data-insights](), [digital-trust](), [fraud](), [integrity](), [prediction-markets](), [trust]() ### AI overview Part 2 examines whether prediction markets can reliably produce trustworthy information. It compares Kalshi's centralized contract resolution with Polymarket's decentralized, oracle-based model and discusses risks involving contract interpretation, voting power, concentrated profits, and market integrity. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift. ## 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