TransUnion (NYSE: TRU) announced expanded machine learning capabilities for its Device Risk solution on March 18, 2026, boosting device recognition, non-human activity detection, and consortium-driven insights to reduce fraud friction and improve detection accuracy.
The company cites ML can improve fraud capture by up to 50% and references survey and trend data showing growing digital fraud losses and account-takeover increases.
This announcement highlights TransUnion’s efforts to combat digital fraud by embedding advanced mach...
Analysis
This announcement highlights TransUnion’s efforts to combat digital fraud by embedding advanced machine learning into its Device Risk solution. With surveyed firms citing $534 billion in fraud losses and an average 7.7% revenue impact, the new models aim to lift fraud capture by up to 50%. Investors may track adoption of these capabilities, future AI-related launches, and how such innovations support the company’s broader growth ambitions.
Key Figures
Reported fraud losses:$534 billionSurvey respondents:1,200 business leadersRevenue lost to fraud:7.7%+3 more
6 metrics
Reported fraud losses$534 billionFraud losses reported by 1,200 business leaders in TransUnion survey
Survey respondents1,200 business leadersTransUnion survey on digital fraud impact
Revenue lost to fraud7.7%Average equivalent annual revenue lost to fraud over past year
Account takeover increase141%Increase in suspected digital account takeovers from H1 2024 to H1 2025
Fraud at account creation26%Increase in suspected digital fraud at account creation over same period
Fraud capture improvement50%ML demonstrated ability to improve fraud capture by up to this amount
Launched AI Analytics Orchestrator Agent with Google Cloud for faster credit analytics.
24h Move is the share-price change in the day after each event; other market factors may also have contributed.
Pattern Detected
AI-focused announcements have previously coincided with positive price reactions for TRU.
Recent Company History
Recent news flow highlights TransUnion’s push to embed AI across its platform and solutions. On March 5, 2026, the company launched its AI Analytics Orchestrator Agent with Google Cloud, aimed at accelerating governed credit analytics, which saw a +1.2% next-day move. Today’s Device Risk machine learning enhancements extend that AI narrative into fraud prevention and device intelligence, reinforcing the company’s broader AI-driven innovation strategy.
Historical Comparison
+1.2% avg move · Past AI-tagged news for TRU saw an average move of 1.2%. Today’s AI-driven Device Risk upgrade and i...
AI
+1.2%
Average Historical MoveAI
Past AI-tagged news for TRU saw an average move of 1.2%. Today’s AI-driven Device Risk upgrade and its 4.17% gain represent a stronger reaction than prior AI disclosures.
AI initiatives have progressed from analytics workflow orchestration with Google Cloud to applying advanced machine learning for device-level fraud detection within Device Risk.
Regulatory & Risk Context
Short Interest: 5.13%
Short Interest
5.13% of shares outstanding
as of 2026-05-29Days to cover: 4.98
Key Terms
machine learning, virtual machines, residential proxies, remote desktops, +2 more
6 terms
machine learningtechnical
"expanded machine learning (ML) capabilities within its Device Risk solution"
Machine learning is a set of computer programs that learn patterns from large amounts of data and improve their predictions or decisions over time, like a recipe that gets better each time it’s adjusted based on taste tests. For investors it matters because these systems can speed up analysis, spot trends or risks humans might miss, automate routine work, and potentially create competitive advantages or cost savings that affect a company’s performance.
virtual machinestechnical
"non-human activity (including behavior patterns associated with virtual machines, residential"
A virtual machine is a software-created computer that runs inside a physical server, allowing one piece of hardware to behave like many separate computers. For investors, virtual machines matter because they enable companies to scale computing capacity, reduce costs, and deploy new services quickly—similar to renting multiple apartments inside a single building instead of buying separate houses, which can improve efficiency and influence profitability and capital needs.
residential proxiestechnical
"behavior patterns associated with virtual machines, residential proxies and remote desktops"
Residential proxies are internet routing services that make a user’s connection appear to come from a real home IP address assigned by an internet service provider, using devices or routers located in ordinary residences. Investors should care because these tools can unlock or mask access to geo-restricted content, large-scale web data collection, or ad verification, affecting revenue, competitive data gathering and legal or compliance risk—think of them as renting a local address to look like a neighborhood resident online.
remote desktopstechnical
"virtual machines, residential proxies and remote desktops)"
Remote desktops are tools that let a person see and control a computer or work environment that sits somewhere else, over the internet, as if they were sitting in front of it — like using a steering wheel from the passenger seat. Investors care because remote desktops affect how efficiently a business supports remote work, how it spends on IT and security, and whether software or services tied to remote access generate steady revenue or expose the company to cyber risk.
device fingerprintingtechnical
"Traditional device fingerprinting has been impacted by privacy-driven technology changes"
Device fingerprinting is a technique that collects non-personal details about a phone, tablet, or computer — such as browser settings, installed fonts, screen size, and hardware attributes — to create a unique digital ID that can recognize that device on future visits. For investors, it matters because it affects user tracking, fraud detection, privacy compliance, and advertising effectiveness: stronger or weaker fingerprinting capabilities can change customer acquisition costs, regulatory risk, and the reliability of online metrics, similar to how a vehicle’s license plate lets you spot the same car over time.
digital account takeoverstechnical
"the volume of digital account takeovers and the rate of suspected digital fraud"
Digital account takeovers occur when an unauthorized person gains control of an individual’s or business’s online account — such as banking, brokerage, email, or customer portals — and uses it as if they were the rightful owner. For investors, this matters because takeovers can cause direct financial losses, costly cleanup, regulatory fines and lasting damage to customer trust and a company’s value, similar to a break‑in that forces expensive repairs and drives customers away.
Enhancements Strengthen Device Intelligence to Protect Consumers and Businesses in an Evolving Threat Landscape
LAS VEGAS, March 18, 2026 (GLOBE NEWSWIRE) -- Suspected digital fraud continues to impact businesses worldwide. In a recent TransUnion (NYSE: TRU) survey of 1,200 business leaders, respondents reported fraud losses totaling $534 billion. To help companies combat this growing threat, TransUnion today announced expanded machine learning (ML) capabilities within its Device Risk solution.
The enhancements are designed to help organizations detect and combat increasingly sophisticated attacks, while maintaining a streamlined and trusted customer experience. Today’s announcement comes at the Merchant Risk Council’s MRC 2026 conference in Las Vegas, where TransUnion will be exhibiting its fraud solutions at Booth 422.
To help businesses stay ahead of emerging threats, TransUnion Device Risk has been further powered to enable:
Stronger recognition of returning devices across customers
More robust detection of non-human activity (including behavior patterns associated with virtual machines, residential proxies and remote desktops)
Deeper consortium-driven insights that illuminate evolving fraud trends
These updates enhance fraud-detection accuracy and streamline digital customer experiences by reducing unnecessary friction. The new capabilities introduce advanced machine learning that extends Device Risk intelligence far beyond traditional, static rule-based decisioning.
Pre-built adaptive ML models learn from thousands of device signals and fraud feedback sourced from TransUnion’s long-standing global fraud consortium. This enables proactive detection of anomalies and evasion attempts. ML has demonstrated the ability to improve fraud capture by up to 50%, while also reducing the volume and complexity of manually maintained rules, lowering operational overhead, and improving overall precision.
“Traditional device fingerprinting has been impacted by privacy-driven technology changes and evolving tactics that let fraudsters look like ‘new’ users with just a few clicks,” said Steve Yin, global head of fraud at TransUnion. “We need to meet this moment with solutions that learn continuously, adapt in real time, and connect more signals across more browsers and applications. This will enable more effective recognition of risky behavior even as identifiers change. These enhancements mark a significant advancement in how device-level intelligence is used to secure digital interactions across industries.”
Digital Fraud Rising Across the Globe
Digital fraud continues to expand across the global economy. According to TransUnion’s H2 2025 Update to its Top Fraud Trends Report, organizations lost an average of 7.7% of equivalent annual revenue to fraud over the past year. At the same time, the volume of digital account takeovers and the rate of suspected digital fraud tied to account creation both increased over the last year.
According to a TransUnion analysis, suspected digital account takeovers increased by 141% between H1 2024 and H1 2025. Over the same period, suspected digital fraud at account creation grew by 26%. These trends underscore the need for more adaptive, precise and intelligent device recognition capabilities.
“Our Device Risk enhancements demonstrate how TransUnion innovates to stay a step ahead of advanced fraud tactics by pairing richer device-level intelligence with adaptive machine learning,” said Clint Lowry, vice president of global fraud solutions at TransUnion. “By elevating both detection and efficiency, we empower customers to operate with greater confidence across login, transaction and account creation experiences.”
To learn more about TransUnion Device Risk, click here. To learn more about TransUnion fraud solutions, click here.
TransUnion is a global information and insights company with over 13,000 associates operating in more than 30 countries. We make trust possible by ensuring each person is reliably represented in the marketplace. We do this with a Tru™ picture of each person: an actionable view of consumers, stewarded with care. Through our acquisitions and technology investments we have developed innovative solutions that extend beyond our strong foundation in core credit into areas such as marketing, fraud, risk and advanced analytics. As a result, consumers and businesses can transact with confidence and achieve great things. We call this Information for Good® — and it leads to economic opportunity, great experiences and personal empowerment for millions of people around the world.
What did TransUnion (TRU) announce about Device Risk on March 18, 2026?
TransUnion announced expanded machine learning capabilities for Device Risk to strengthen device recognition and non-human detection. According to TransUnion, the enhancements add adaptive ML models, consortium-sourced signals, and improved anomaly detection to reduce fraud friction and boost accuracy.
How much can the new ML in TransUnion Device Risk improve fraud detection for TRU customers?
The company reports ML can improve fraud capture by up to 50% for Device Risk customers. According to TransUnion, pre-built adaptive models learn from thousands of device signals and fraud feedback to detect evasive behavior more proactively.
What fraud trends did TransUnion cite when unveiling TRU Device Risk updates?
TransUnion cited rising fraud losses and takeover rates, including a 7.7% average revenue loss to fraud. According to TransUnion, suspected account takeovers rose 141% between H1 2024 and H1 2025, with account-creation fraud up 26%.
How do the Device Risk enhancements affect customer experience for companies using TRU solutions?
The enhancements aim to reduce unnecessary friction while improving detection precision across login, transaction, and account creation flows. According to TransUnion, adaptive ML and richer signals allow more accurate decisions and fewer manual rules.
Where and when did TransUnion announce the Device Risk machine learning enhancements?
TransUnion announced the Device Risk enhancements on March 18, 2026, at the Merchant Risk Council MRC 2026 conference in Las Vegas. According to TransUnion, the company exhibited its fraud solutions at Booth 422 during the event.
