# Digital Trust

An operational discipline for ensuring digital systems and services are secure, reliable, resilient, and accountable.

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## 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](<https://devfeed.tech/articles/building-trust-in-a-digital-world-the-role-of-machine-learning-in-behavioral-biometrics-26295.md>)

Original publisher: [Read original article](<https://medium.com/feedzaitech/building-trust-in-a-digital-world-the-role-of-machine-learning-in-behavioral-biometrics-bb0da913d95a?source=rss----e11168e7fe6b---4>)

Author: Javier Liébana

Published: 2024-06-21T14:01:53Z

Content type: article

Language: en

Sources: [Feedzai](<https://devfeed.tech/sources/feedzai.md>)

Topics: [Digital Trust](<https://devfeed.tech/topics/digital-trust.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Machine Learning, Security Attacks](<https://devfeed.tech/topics/machine-learning-security-attacks.md>)

Tags: [core](<https://devfeed.tech/tags/core.md>), [digital-trust](<https://devfeed.tech/tags/digital-trust.md>), [feedzai](<https://devfeed.tech/tags/feedzai.md>), [fraud-detection](<https://devfeed.tech/tags/fraud-detection.md>), [fraud-prevention](<https://devfeed.tech/tags/fraud-prevention.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [online-fraud-prevention](<https://devfeed.tech/tags/online-fraud-prevention.md>), [research](<https://devfeed.tech/tags/research.md>)

### 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