TransUnion Research Highlights Power of Public Data in Uncovering $3.3B Synthetic Identity Threat
TransUnion (NYSE:TRU) has released new research highlighting how public data can help detect synthetic identity fraud, which has exposed U.S. lenders to over $3.3 billion in potential losses for the year ending 2024.
The study reveals that certain missing real-world attributes can indicate synthetic identities, with factors like absent vehicle registrations and no known relatives occurring in 30-50% of synthetic identities, increasing fraud likelihood by up to 7x. TransUnion's Synthetic Fraud Model leverages these public data indicators to help financial institutions identify fraudulent identities before they cause financial harm.
Key red flags include missing voter registrations, no property ownership records, and notably, no open bankruptcies - a universal trait among synthetic identities.
TransUnion (NYSE:TRU) ha pubblicato una nuova ricerca che mette in evidenza come i dati pubblici possano aiutare a rilevare le frodi da identità sintetica, esponendo i prestatori statunitensi a oltre 3,3 miliardi di dollari in potenziali perdite per l’anno chiuso nel 2024.
Lo studio mostra che alcune mancanze di attributi del mondo reale possono indicare identità sintetiche, con fattori come l’assenza di registrazioni dei veicoli e l’assenza di parenti noti presenti nel 30-50% delle identità sintetiche, aumentando la probabilità di frode fino a 7 volte.
Il Modello di Frode Sintetica di TransUnion sfrutta questi indicatori di dati pubblici per aiutare le istituzioni finanziarie a identificare identità fraudolente prima che causino danni finanziari.
Tra i segnali di allarme principali vi sono la mancanza di registrazioni elettorali, l’assenza di registri di proprietà e, in particolare, l’assenza di fallimenti aperti — una caratteristica comune tra le identità sintetiche.
TransUnion (NYSE:TRU) ha publicado una nueva investigación que destaca cómo los datos públicos pueden ayudar a detectar el fraude de identidad sintética, que ha expuesto a los prestamistas estadounidenses a más de 3,3 millardos de dólares en pérdidas potenciales para el año que terminó en 2024.
El estudio revela que ciertos atributos del mundo real que faltan pueden indicar identidades sintéticas, con factores como la ausencia de registros de vehículos y la falta de parientes conocidos que ocurren en 30-50% de las identidades sintéticas, aumentando la probabilidad de fraude hasta 7 veces.
El Modelo de Fraude Sintético de TransUnion aprovecha estos indicadores de datos públicos para ayudar a las instituciones financieras a identificar identidades fraudulentas antes de que causen daños financieros.
Las señales clave incluyen la ausencia de registros de votantes, la ausencia de registros de propiedad y, notablemente, la ausencia de quiebras abiertas — una característica universal entre las identidades sintéticas.
TransUnion (NYSE:TRU)는 공공 데이터가 합성 신원 사기를 탐지하는 데 어떻게 도움이 되는지에 대한 새로운 연구를 발표했으며, 이는 2024년을 마감하는 기간 동안 미국 대출기관에 33억 달러 이상의 잠재적 손실 위험을 초래했다.
연구에 따르면 차량 등록 부재와 알려진 친척이 없는 등 현실 세계의 특정 속성이 합성 신원을 나타낼 수 있으며, 이는 합성 신원의 30-50%에서 관찰되고 사기 가능성을 최대 7배까지 높인다.
TransUnion의 합성 사기 모델은 이러한 공공 데이터 지표를 활용하여 금융기관이 재정적 피해를 주기 전에 fraudulent identity를 식별하는 데 도움을 준다.
주요 경고 신호로는 유권자 등록 부재, 재산 소유권 기록 부재, 특히 열려 있는 파산 기록이 없다는 점이 있는데—합성 신원 사이에서 보편적인 특징이다.
TransUnion (NYSE:TRU) a publié de nouvelles recherches montrant comment les données publiques peuvent aider à détecter la fraude d’identité synthétique, qui a exposé les prêteurs américains à plus de 3,3 milliards de dollars de pertes potentielles pour l’année se terminant en 2024.
L’étude révèle que certains attributs du monde réel manquants peuvent indiquer des identités synthétiques, des facteurs tels que l’absence d’enregistrements de véhicules et l’absence de proches connus apparaissant dans 30-50% des identités synthétiques, augmentant la probabilité de fraude jusqu’à 7 fois.
Le Modèle de Fraude Synthétique de TransUnion s’appuie sur ces indicateurs issus des données publiques pour aider les institutions financières à identifier les identités frauduleuses avant qu’elles n’occasionnent des dommages financiers.
Les signaux d’alerte clés incluent l’absence d’enregistrements électoraux, l’absence d’enregistrements de propriété et, notamment, l’absence de faillites ouvertes — une caractéristique universelle parmi les identités synthétiques.
TransUnion (NYSE:TRU) hat neue Forschungen veröffentlicht, die aufzeigen, wie öffentliche Daten helfen können, synthetische Identitätsbetrug zu erkennen, der US-Kreditgeber im Geschäftsjahr 2024 potenziell über 3,3 Milliarden Dollar an Verlusten ausgesetzt hat.
Die Studie zeigt, dass fehlende reale Attribute synthetische Identitäten anzeigen können, wobei Faktoren wie fehlende Fahrzeugregistrierungen und keine bekannten Verwandten in 30-50% der synthetischen Identitäten auftreten und die Betrugswahrscheinlichkeit um bis zu 7-mal erhöhen.
TransUnion's Synthetic Fraud Model nutzt diese öffentlichen Datenindikatoren, um Finanzinstitute dabei zu unterstützen, betrügerische Identitäten zu identifizieren, bevor sie finanziellen Schaden verursachen.
Zu den wichtigsten Warnzeichen gehören fehlende Wählerregistrierungen, kein Eigentumsnachweis und insbesondere keine offenen Insolvenzen – ein universelles Merkmal unter synthetischen Identitäten.
TransUnion (NYSE:TRU) نشرت أبحاثًا جديدة تسلط الضوء على كيفية مساهمة البيانات العامة في اكتشاف الاحتيال بالهوية التركيبية، مما عرض المقرضين الأميركيين لخسائر محتملة تفوق 3.3 مليار دولار للسنة المنتهية في 2024.
تكشف الدراسة أن بعض السمات الواقعية المفقودة يمكن أن تشير إلى الهويات التركيبية، مع عوامل مثل غياب تسجيلات المركبات وعدم وجود أقارب معروفين تظهر في 30-50% من الهويات التركيبية، مما يزيد احتمال الاحتيال حتى 7 أضعاف.
يستفيد نموذج الاحتيال التركيبي من ترانزيون من هذه المؤشرات المستمدة من البيانات العامة لمساعدة المؤسسات المالية على تحديد الهويات الاحتيالية قبل أن تتسبب في ضرر مالي.
تشمل العلامات الحمراء الرئيسية غياب تسجيلات الناخبين، وغياب سجلات الملكية، وبشكل خاص عدم وجود إفلاسات مفتوحة - وهي سمة شائعة بين الهويات التركيبية.
TransUnion（NYSE:TRU） 发布了新的研究，强调公共数据如何帮助检测合成身份欺诈，这使美国贷款机构在截至2024年的年度中面临超过 33亿美元 的潜在损失。
研究显示，某些现实世界属性的缺失可能指示合成身份，其中如车辆登记缺失和无知名亲属在 30-50% 的合成身份中出现，从而将欺诈可能性提升至最多 7倍。
TransUnion 的合成欺诈模型利用这些公共数据指标，帮助金融机构在造成财务损失前识别欺诈身份。
关键警示信号包括缺少选民登记、缺少房产登记，以及尤其没有开放破产记录——这是合成身份中的普遍特征。
- Development of advanced synthetic identity detection system using public data indicators
- Model helps prevent fraud while reducing manual reviews and customer friction
- Solution enables detection of fraud risk throughout the entire customer lifecycle
- U.S. lenders faced over $3.3 billion in synthetic identity fraud exposure in 2024
- Synthetic identities are becoming increasingly sophisticated and harder to detect
- Traditional identity verification systems are proving inadequate against synthetic fraud
Insights
TransUnion reveals $3.3B synthetic identity fraud threat, highlighting how public data markers can detect fraudulent identities before financial damage occurs.
TransUnion's research quantifies the substantial financial threat posed by synthetic identity fraud, with U.S. lenders facing over
The company's analysis identifies specific public data markers that significantly increase the likelihood of detecting synthetic identities. The absence of certain "living characteristics" serves as powerful indicators - identities lacking known relatives or motor vehicle registrations are up to 7 times more likely to be synthetic. Other red flags include missing voter registrations and property ownership records.
What makes this research particularly valuable is how it addresses the sophistication of modern synthetic identity fraud. These aren't crude attempts - they're engineered to mimic legitimate consumer behavior patterns and often bypass traditional verification systems. TransUnion's approach leverages multiple data points to form a comprehensive identity picture, rather than relying on single verification methods.
The company's Synthetic Fraud Model represents a practical application of this research, using public data indicators alongside other risk factors to identify synthetic identities earlier in the customer journey. This proactive approach promises dual benefits: reduced fraud exposure for financial institutions and improved operational efficiency through fewer manual reviews and decreased customer friction.
For investors, this represents TransUnion positioning itself as an essential service provider in the growing fraud prevention market, directly addressing a multi-billion dollar problem that traditional methods struggle to detect.
New analysis shows how missing real-world attributes—like voter registration, vehicle ownership, and familial ties—can help lenders detect synthetic identities and reduce fraud exposure
CHICAGO, Sept. 17, 2025 (GLOBE NEWSWIRE) -- With synthetic identities now linked to a record number of newly opened accounts, U.S. lenders faced more than
Synthetic identities are carefully constructed using a blend of authentic and fabricated information—often incorporating stolen Social Security numbers, fictitious names, digital contact details and behavioral patterns that mimic legitimate consumer activity. These identities are engineered to appear credible and frequently bypass traditional identity verification systems, making them particularly difficult to detect using conventional methods.
There is no single blueprint for how criminals perpetrate synthetic identity fraud, which adds to its complexity. Increasingly, organizations face the challenge of distinguishing genuine customers from synthetic ones, especially when these false identities exhibit consistent, low-risk behavior that closely mimics that of real individuals. To stay ahead of evolving threats, organizations must leverage advanced detection tools capable of isolating and analyzing specific traits, behavioral patterns and characteristics that are frequently associated with synthetic identities.
“While the presence of living characteristics such as vehicle ownership, voter registration or familial connections is not a definitive solution to detecting synthetic identities, it represents an important piece of the broader identity puzzle,” said Steve Yin, senior vice president and global head of fraud at TransUnion. “These attributes alone cannot confirm authenticity, but when combined with credit header data, they offer valuable context that contributes to forming a clear picture of identity. By isolating and evaluating these elements, organizations can strengthen their ability to differentiate between real and synthetic identities with greater precision.”
There are a number of living characteristics that, when present, indicate an identity to be significantly more likely to be synthetic. For example, no known relatives and no motor vehicle registrations occur in 30
TransUnion’s Synthetic Fraud Model is designed to proactively identify a wide range of public data indicators, along with numerous other risk factors, to help uncover synthetic identities before they can cause financial harm. By analyzing these signals early in the customer journey, the model enables organizations to take preventive action with greater confidence and precision.
At the same time, the model enhances operational efficiency by reducing the need for manual reviews and minimizing customer friction. This allows lenders to streamline their processes while improving fraud detection rates—catching more fraudulent activity with greater accuracy and speed, and ultimately protecting both their customers and their bottom line.
Yin added, “Just as fraudsters relentlessly exploit every tactic available to pursue their deceptive financial objectives, lenders must be equally vigilant and proactive in their defense. Solutions like TransUnion’s Synthetic Fraud Model empower lenders to detect risk at every stage of the customer lifecycle—starting with account creation—by identifying the absence of real-life attributes, helping to prevent fraud and minimize financial losses.”
To learn more about strategies to protect from digital risk with a clear picture of identity, click here. To learn more about how TransUnion’s TruValidate Synthetic Fraud Model can help lenders detect synthetic identity fraud while increasing approval rates for legitimate customers throughout the customer lifecycle, click here.
About TransUnion (NYSE: TRU)
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. http://www.transunion.com/business.
| Contact | Dave Blumberg |
| TransUnion | |
| david.blumberg@transunion.com | |
| Telephone | 312-972-6646 |
FAQ
How much synthetic identity fraud exposure did U.S. lenders face in 2024?
What are the key indicators of synthetic identity fraud according to TransUnion's research?
How much more likely is an identity to be synthetic when missing certain characteristics?
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Why are synthetic identities difficult to detect using traditional methods?
