This article explains why synthetic identities are difficult to detect, where traditional verification falls short, and how businesses can build stronger prevention strategies without adding unnecessary friction for legitimate users.<\/p>\n\n\n\n
What is a Synthetic Identity Used For?<\/strong><\/h2>\n\n\n\n![\"Synthetic](\"https:\/\/ondato.com\/wp-content\/uploads\/2025\/08\/v1_2026-07_Synthetic_Identity_Fraud_Featured-1-1024x578.webp\")
<\/figure>\n\n\n\nSynthetic identities can serve many criminal purposes and types of fraud<\/a>. Once established, they become powerful tools for fraudsters to exploit financial systems and beyond.<\/p>\n\n\n\nCredit Card Fraud<\/strong><\/h3>\n\n\n\nOne of the most widespread uses of synthetic identities is to apply for credit cards or personal bank loans. Fraudsters often take care of these accounts responsibly by making small purchases first and paying them off to establish credibility.\u00a0<\/p>\n\n\n\n
But, once trust is built, they \u00abbust out\u00bb, maxing out credit cards or defaulting on loans before disappearing. Because the identity doesn\u2019t belong to a real person, there is no one to pursue for repayment, leaving financial institutions with unrecoverable losses.<\/p>\n\n\n\n
Money Laundering<\/strong><\/h3>\n\n\n\nSynthetic identities are also a convenient tool for laundering illicit funds. Fraudsters open multiple accounts<\/a> under different synthetic profiles, moving money back and forth to obscure its origins. By layering transactions through accounts that appear legitimate on paper, criminals can integrate illegal profits into the financial system without attracting suspicion<\/a>, making it harder for regulators and investigators to trace.<\/p>\n\n\n\nGovernment Benefit Fraud<\/strong><\/h3>\n\n\n\nGovernment programs, such as unemployment benefits, social security, or stimulus relief funds, are common targets for synthetic identity schemes. By using fabricated identities, fraudsters can file multiple fraudulent claims, siphoning money intended for vulnerable populations. The large scale and high volume of such programs make them especially vulnerable, often resulting in billions of dollars in taxpayer losses.<\/p>\n\n\n\n
Healthcare Fraud<\/strong><\/h3>\n\n\n\nIn the healthcare sector, synthetic identities may be used to obtain medical services, expensive treatments, or prescription drugs. This can range from fraudsters seeking free care to criminal groups acquiring controlled substances for resale. Beyond financial costs, synthetic healthcare fraud risks create corrupted medical records tied to non-existent patients, which can cause confusion or even harm when linked to real individuals.<\/p>\n\n\n\n
Why Synthetic Identities Are So Difficult to Detect<\/strong><\/h2>\n\n\n\n![\"Synthetic](\"https:\/\/ondato.com\/wp-content\/uploads\/2025\/08\/v1_2026-07_Synthetic_Identity_Fraud_Featured-2-1024x578.webp\")
<\/figure>\n\n\n\nSynthetic identity fraud is difficult to detect because the customer may look real enough to pass basic checks, but not real enough to exist as a legitimate person.<\/p>\n\n\n\n
The US Federal Reserve<\/a> describes synthetic identity fraud as the use of real and\/or fictitious information to create a fake identity for fraud, often making it harder for existing systems to detect.<\/p>\n\n\n\nLet\u2019s start by clarifying the difference between a traditional and a synthetic identity fraud:<\/p>\n\n\n\n
\n- Traditional identity theft <\/strong>usually involves impersonating one real individual.<\/li>\n\n\n\n
- Synthetic identity fraud<\/strong> involves creating a new identity by combining real and fictional information, such as a real Social Security Number with a fake name, date of birth, address, phone number, or email.<\/li>\n<\/ul>\n\n\n\n
This difference matters. When a real person\u2019s identity is stolen, they may notice suspicious activity, dispute an account, freeze their credit, or report fraud. With synthetic identity fraud, there may be no immediate victim raising the alarm because the fabricated identity does not fully belong to one real person.<\/p>\n\n\n\n
EXAMPLE:<\/strong> A fraudster uses a real child\u2019s Social Security Number<\/a> (SNN), a fake adult name, a rented address, and a newly created phone number. At first, the application may look \u201cthin\u201d rather than suspicious. The fraudster opens a low-risk account, behaves normally, builds history, applies for higher limits, and eventually disappears after maxing out credit or moving funds.<\/em><\/p>\n\n\n\nThat is what makes synthetic fraud so dangerous, as it’s not always a quick attack. It can be a slow build.<\/p>\n\n\n\n
Synthetic identities can pass early checks and become risky later. The fraudster is not only trying to pass onboarding but to create a durable identity that can survive multiple checks across banks, fintechs, lenders, telecoms, marketplaces, and digital platforms.<\/p>\n\n\n\n
\n\u00abSynthetic identity theft is one of the more troubling forms of identity theft and has been a focus of some of our recent investigations. It is a unique form of fraud that combines SSNs of real people with fraudulent information, such as false names and dates of birth, to create new identities.\u00bb \u2013\u00a0 <\/em>Jeffrey Brown, Deputy Assistant Inspector General, Office of the US Inspector General<\/em><\/strong><\/a>.\u00a0<\/em><\/p>\n<\/blockquote>\n\n\n\nWhere Traditional Identity Verification Falls Short<\/strong><\/h2>\n\n\n\nEvery verification method is useful in itself. Yet, relying on just one method as if it can answer every fraud question is a grave mistake.<\/p>\n\n\n\n
Here is what traditional verification methods can and cannot do:<\/p>\n\n\n\n
\n- A <\/strong>document check<\/strong><\/a> can confirm whether an ID document appears genuine, but it may not prove that the entire identity history is legitimate.<\/li>\n\n\n\n
- A database check<\/strong> can confirm that certain attributes match records, but it may miss a newly assembled identity with just enough real information to pass.<\/li>\n\n\n\n
- A credit bureau match<\/strong> can be helpful, but synthetic identities are often designed to slowly build credit history over time.<\/li>\n\n\n\n
- A selfie or <\/strong>biometric check<\/strong><\/a> (liveness detection) can confirm that a person is present, but it does not automatically prove that the identity behind that face is legitimate.<\/li>\n<\/ul>\n\n\n\n
And now, let\u2019s compare common verification layers:<\/p>\n\n\n\n
<\/figure>\n\n\n\nSynthetic identities can serve many criminal purposes and types of fraud<\/a>. Once established, they become powerful tools for fraudsters to exploit financial systems and beyond.<\/p>\n\n\n\n One of the most widespread uses of synthetic identities is to apply for credit cards or personal bank loans. Fraudsters often take care of these accounts responsibly by making small purchases first and paying them off to establish credibility.\u00a0<\/p>\n\n\n\n But, once trust is built, they \u00abbust out\u00bb, maxing out credit cards or defaulting on loans before disappearing. Because the identity doesn\u2019t belong to a real person, there is no one to pursue for repayment, leaving financial institutions with unrecoverable losses.<\/p>\n\n\n\n Synthetic identities are also a convenient tool for laundering illicit funds. Fraudsters open multiple accounts<\/a> under different synthetic profiles, moving money back and forth to obscure its origins. By layering transactions through accounts that appear legitimate on paper, criminals can integrate illegal profits into the financial system without attracting suspicion<\/a>, making it harder for regulators and investigators to trace.<\/p>\n\n\n\n Government programs, such as unemployment benefits, social security, or stimulus relief funds, are common targets for synthetic identity schemes. By using fabricated identities, fraudsters can file multiple fraudulent claims, siphoning money intended for vulnerable populations. The large scale and high volume of such programs make them especially vulnerable, often resulting in billions of dollars in taxpayer losses.<\/p>\n\n\n\n In the healthcare sector, synthetic identities may be used to obtain medical services, expensive treatments, or prescription drugs. This can range from fraudsters seeking free care to criminal groups acquiring controlled substances for resale. Beyond financial costs, synthetic healthcare fraud risks create corrupted medical records tied to non-existent patients, which can cause confusion or even harm when linked to real individuals.<\/p>\n\n\n\n Synthetic identity fraud is difficult to detect because the customer may look real enough to pass basic checks, but not real enough to exist as a legitimate person.<\/p>\n\n\n\n The US Federal Reserve<\/a> describes synthetic identity fraud as the use of real and\/or fictitious information to create a fake identity for fraud, often making it harder for existing systems to detect.<\/p>\n\n\n\n Let\u2019s start by clarifying the difference between a traditional and a synthetic identity fraud:<\/p>\n\n\n\n This difference matters. When a real person\u2019s identity is stolen, they may notice suspicious activity, dispute an account, freeze their credit, or report fraud. With synthetic identity fraud, there may be no immediate victim raising the alarm because the fabricated identity does not fully belong to one real person.<\/p>\n\n\n\n EXAMPLE:<\/strong> A fraudster uses a real child\u2019s Social Security Number<\/a> (SNN), a fake adult name, a rented address, and a newly created phone number. At first, the application may look \u201cthin\u201d rather than suspicious. The fraudster opens a low-risk account, behaves normally, builds history, applies for higher limits, and eventually disappears after maxing out credit or moving funds.<\/em><\/p>\n\n\n\n That is what makes synthetic fraud so dangerous, as it’s not always a quick attack. It can be a slow build.<\/p>\n\n\n\n Synthetic identities can pass early checks and become risky later. The fraudster is not only trying to pass onboarding but to create a durable identity that can survive multiple checks across banks, fintechs, lenders, telecoms, marketplaces, and digital platforms.<\/p>\n\n\n\n \u00abSynthetic identity theft is one of the more troubling forms of identity theft and has been a focus of some of our recent investigations. It is a unique form of fraud that combines SSNs of real people with fraudulent information, such as false names and dates of birth, to create new identities.\u00bb \u2013\u00a0 <\/em>Jeffrey Brown, Deputy Assistant Inspector General, Office of the US Inspector General<\/em><\/strong><\/a>.\u00a0<\/em><\/p>\n<\/blockquote>\n\n\n\n Every verification method is useful in itself. Yet, relying on just one method as if it can answer every fraud question is a grave mistake.<\/p>\n\n\n\n Here is what traditional verification methods can and cannot do:<\/p>\n\n\n\n And now, let\u2019s compare common verification layers:<\/p>\n\n\n\nCredit Card Fraud<\/strong><\/h3>\n\n\n\n
Money Laundering<\/strong><\/h3>\n\n\n\n
Government Benefit Fraud<\/strong><\/h3>\n\n\n\n
Healthcare Fraud<\/strong><\/h3>\n\n\n\n
Why Synthetic Identities Are So Difficult to Detect<\/strong><\/h2>\n\n\n\n
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Where Traditional Identity Verification Falls Short<\/strong><\/h2>\n\n\n\n
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![\"Synthetic](\"https:\/\/ondato.com\/wp-content\/uploads\/2025\/08\/v1_2026-07_Synthetic_Identity_Fraud_Featured-1-copy-1024x578.webp\")
<\/figure>\n\n\n\n![\"Verification](\"https:\/\/ondato.com\/wp-content\/uploads\/2024\/07\/v01_2024-07_Synthetic-Identity-Fraud_Banner-1-1024x299.webp\")
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