The phrase "synthetic identity" gets people nervous. It shouldn't. There is a hard line between a profile generated from scratch by an algorithm and data ripped from a real person's credit file, and that line matters far more than most coverage of this topic is willing to admit.
Invented, Not Stolen
A synthetic identity is a persona built entirely from generated data. Name, address, date of birth, phone number, financial details, employer: every field is created algorithmically. No real person's information is copied, harvested, or repurposed. The identity is fictional from top to bottom. A person who does not and has never existed.
This sounds straightforward because it is. The confusion comes from a completely different activity that happens to share the same name.
The Terminology Problem
"Synthetic identity fraud" refers to a specific criminal technique: taking a real identifier, typically a Social Security number belonging to a child, an elderly person, or someone with a thin credit file, and building fabricated details around it. The resulting profile blends real and fake data. Criminals use it to open credit accounts, accumulate a credit history over months or years, and eventually "bust out" for a large balance before disappearing.
The Federal Reserve published a widely cited paper on this in 2019, estimating annual losses at $6 billion in the US alone. That number shows up in headline after headline. What rarely appears alongside it is the fact that the $6 billion figure refers specifically to fraud involving blended real-and-fake data. It has nothing to do with generating entirely fictional profiles.
But the phrase is the same. "Synthetic identity." So when a QA engineer mentions needing synthetic identities for a test suite, the reaction from non-technical stakeholders sometimes lands closer to alarm than it should. That's a terminology problem, not a technology problem, and it's been making conversations about legitimate synthetic data needlessly awkward for years.
Coverage rarely helps. A 2023 Thomson Reuters report on synthetic identity fraud used the phrase "synthetic identity" dozens of times without once distinguishing between blended-data fraud and purely generated profiles. McKinsey's 2024 identity verification report did the same. Both are solid work on the fraud problem they're actually describing. The collateral damage is that anyone searching for "synthetic identity tools" gets three pages of fraud prevention articles before finding anything about legitimate data generation. The search results around this topic are thoroughly contaminated by the terminology overlap.
How Synthetic Identity Generation Actually Works
The technical process behind a legitimate synthetic identity is less mysterious than the fraud headlines suggest. A generator does three things: picks values for each field, checks those values against each other for consistency, and formats the output according to regional conventions.
Field generation starts with constraints. A name appropriate for the selected country. An age within a specified range. A phone number in the correct format. An address that follows the postal conventions of the chosen region. The simplest generators treat each field independently, which produces profiles that look plausible in isolation but collapse under scrutiny: a French name, an American phone number, a postcode from neither country.
Better generators cross-reference fields during creation. If the country is Germany, the phone number starts with +49, the postcode is five digits, the street address follows German formatting conventions, and the employer name is plausible for the region. The identity holds together as a believable person from a specific place, which is what makes the data actually useful for testing and research rather than just visually filling form fields.
Financial data generation follows its own rules. Card numbers need to pass Luhn validation, a checksum algorithm that every card processor uses as a basic format check. A generated card number satisfies that check but isn't linked to any real account. It looks correct in a database. It passes client-side form validation. It won't process a real transaction. The same principle applies to National Insurance numbers, Social Security numbers, and other national identifiers: correctly formatted, structurally valid, connected to nobody.
Email generation adds another layer. Some generators only produce email-formatted strings: addresses that look right but don't actually receive mail. More complete tools generate functional email addresses with working inboxes, which transforms the synthetic identity from a data placeholder into something that can interact with real systems. Verification emails arrive. Password resets work. Two-factor codes land in the inbox. The difference between a decorative email string and a functional mailbox is the difference between data that fills a field and data that tests a workflow.
What People Actually Use Them For
The use cases split into three broad categories, and none of them involve committing crimes.
Software testing is the biggest. QA teams need realistic user data to test registration flows, checkout processes, email notifications, and profile management features. Using real customer data in test environments is a GDPR violation in the EU and a liability risk everywhere else. Making up data by hand produces inconsistent, implausible profiles that trigger validation errors and create bugs nobody can reproduce. Synthetic identities solve both problems: the data looks real, behaves correctly in forms, and belongs to nobody.
Within the testing category alone, the variety is wider than most people expect. Localisation teams use country-specific synthetic identities to verify that forms, date formats, and address fields handle international data correctly. Payment teams use generated card numbers to test checkout flows without touching real financial instruments. Growth teams use synthetic signups to check that onboarding sequences trigger correctly. Each scenario needs slightly different data, but they all share the same core requirement: realistic profiles that pass validation and belong to no one.
Security research is the second category. Phishing investigations require email addresses that aren't connected to real people. Social engineering assessments need realistic personas that hold up to casual scrutiny. Honeypot operations need identities that look convincing to threat actors poking at them. The synthetic profile gives the researcher a credible persona without risking anyone's actual data.
Privacy protection rounds out the set. Anyone who's been online long enough to regret handing their real email address to every service that asked has a use for synthetic identities. Sign up for a service you don't fully trust. Use a generated email address instead of your real one. If the service gets breached, or sells your data to a broker, or starts sending three marketing emails a day, the damage stays contained to an address you can walk away from without looking back.
The pattern is usually the same. You want to try a service, but the signup form demands your full name, email address, phone number, and sometimes a physical address before showing you anything. Hand over your real details, and you've given a company you don't trust the building blocks of a marketing profile they can sell to data brokers or lose in the next breach. Use a synthetic identity, and the worst that happens is a fictional person gets spam.
The Coherence Problem
Not all synthetic identity generators produce equally useful output. The difference between a useful tool and a waste of time almost always comes down to coherence.
A random data generator can produce a name and a phone number. Concatenate them. Done. But if that name is "Yuki Tanaka" and the phone number is +1-555-0147 and the address is "47 Rue de la Paix, London," the profile is nonsense. No one is going to believe it's real. A form validator might accept the individual fields, but any downstream system that checks for internal consistency will flag it, and any human reading the test data will spot the problem immediately.
Coherent synthetic identities solve this by treating the profile as a connected whole rather than a collection of independent fields. Country selection determines formatting rules, naming conventions, phone number patterns, and address structures. Age affects plausible employment history. Everything fits together because the generation process enforces internal consistency rather than assembling random pieces.
This matters practically because downstream systems don't exist in isolation. A test that creates a user profile, sends a verification email, processes a payment, and generates a shipping label touches four different services that all need to agree on who the user is. Incoherent synthetic data causes cascading mismatches that waste debugging time and produce test results that don't reflect how the application actually behaves with real users.
Tools of the Trade
For developers, the default starting point is usually a Faker library. Python's Faker, Ruby's FFaker, JavaScript's various faker.js successors. These are solid for generating individual data fields: a fake name here, a fake address there, a plausible phone number. The limitation appears when you need a complete, coherent identity. Faker generates each field independently, so getting a profile where every data point is consistent with a single plausible person means writing the correlation logic yourself.
Faker's library coverage is extensive. Over 60 locales, most common data types covered, clean integration with test frameworks and database seeders. For projects that need high volumes of loosely structured test data, Faker is hard to beat. The gap shows up when the project needs correlated profiles where every field relates to the same fictional person, or when working email addresses are part of the requirement.
Manual creation is what most people do before they find a better approach. Open a spreadsheet, invent a name, make up an address, pick a random date of birth. This works exactly once, for exactly one identity, and falls apart the moment you need twenty profiles for a test suite or five personas for a research project. The inconsistencies pile up faster than anyone expects.
Purpose-built generators like Another.IO sit at the other end. The identity arrives as a complete, correlated profile from a single click or API call. Functional email inbox included. Country-specific formatting handled automatically. No development environment required. The trade-off is less granular control over individual fields compared to writing custom Faker scripts, but for most use cases the time savings make the trade-off easy to accept.
Legal and Ethical Boundaries
Generating fictional data that belongs to nobody is legal in every jurisdiction that has weighed in on the topic. Using that data to test software, conduct authorised security research, or protect personal privacy is legal. The legal problems start when synthetic data gets used to defraud financial institutions, impersonate real people, or circumvent identity verification for malicious purposes.
GDPR and similar privacy regulations actually favour synthetic data in testing contexts. Article 25 of GDPR encourages data minimisation, and using generated data instead of real customer information in development and QA environments is one of the most straightforward compliance strategies available. Several regulatory guidance documents explicitly recommend synthetic data as an alternative to production data in non-production environments.
In the UK, the Information Commissioner's Office has referenced synthetic data favourably in regulatory technology discussions. The European Data Protection Board mentions it as a privacy-enhancing technology. These aren't blanket endorsements, but they signal that regulators understand the difference between generating fictional profiles and misusing real personal data.
The ethical line mirrors the legal one. If the synthetic identity supports testing, research, or privacy protection, it's legitimate. If it's used to deceive for financial gain or to cause harm, it's fraud. The "synthetic" label provides zero legal cover for criminal activity, and nobody should expect it to.
The Short Version
Synthetic identities are generated profiles that belong to nobody. They exist because software needs realistic test data, researchers need disposable personas, and privacy-conscious users need alternatives to handing out their real information to every service that asks for it.
The term has a public relations problem thanks to sharing vocabulary with a specific type of financial fraud. Understanding the distinction matters, because it shapes how non-technical stakeholders react when someone brings up synthetic data in a planning meeting. But the technology itself is clean, the use cases are legitimate, and the line between generated data and stolen data is structural. Not a matter of perspective.