Synthetic identity fraud kept rising in 2024 and the jump was real. One report showed a sharp 60 % surge and that single number is enough to show how fast the threat is growing.
As we step into 2026 the upward trend stays firm and the risk grows for banks and customers at the same time. As fraud tools become smarter and identity checks stay shallow the stakes for businesses and consumers alike are higher than ever.
This guide will walk you step by step through what synthetic identity fraud is, how it works and most importantly how you can build your defences for 2026 and beyond.
What Is Synthetic Identity Fraud?
Synthetic identity fraud is a crime where a new identity is built from pieces that do not belong together. It feels real enough to pass through the system and it gains trust without effort.
This makes synthetic identity fraud detection difficult because there is no direct victim to raise a hand so the fraud grows in silence and hits when no one is ready.
- Real plus fake data mix. A criminal may start with a real Social Security number that belongs to someone who is not using credit and then attach a fresh identity on top of it. The result is a person who does not exist but still looks safe enough to enter the system without trouble.
- No clear victim. The crime goes unnoticed because no single person feels the impact at the start. Nothing triggers a warning and nothing looks out of place. The identity keeps moving like a normal user while the criminal gets time to build a stronger position.
Manipulated vs Manufactured Synthetics
Synthetic identity fraud does not look the same in every case and two patterns appear in most investigations. Experts call them manipulated synthetics and manufactured synthetics and both show up frequently in synthetic identity fraud statistics because they blend truth and fiction inside one profile.
- Manipulated synthetics. Here the base identity is real. One or more details are changed such as the address or birth date or one digit in an ID number. Often the goal is to escape a weak credit history or a bad record rather than to vanish with a big theft in one shot.
- Manufactured synthetics. Here the whole identity is built as a new person. Real data and fake data are mixed or sometimes the details are fully invented. There is no true owner behind the profile.
- How manipulated synthetics behave. Activity often looks like a normal customer with some small twists. Bills may be paid and accounts may stay open for a long time. The risk is still real yet banks can often spot the problem when they compare details with older records for the same person.
- How manufactured synthetics behave. These identities are treated as fresh customers with no real past. Fraudsters build history slowly then hit a high credit line and walk away. Losses are usually bigger because every part of the journey was designed for a final cash out moment. Detection is very tough.
Why Synthetic Identities Are Becoming a Top Threat Vector
Synthetic identity fraud is rising fast because it fits perfectly into the digital world we live in today. Banking moves fast and identity checks stay surface level which makes real time synthetic identity fraud prevention harder and gives criminals room to work.
- Digital first onboarding. Many banks and fintech platforms now open accounts online with fast approval and light verification. The goal is a smooth user experience so deeper checks are skipped.
- Mass data leaks. Huge data breaches have exposed personal information that sits on the dark web. Criminals pick real numbers that belong to people who are not active in credit and attach new details to create a fresh identity.
- Shift from classic fraud. Stolen identity fraud has become harder due to stronger controls so criminals are moving to synthetic identities instead. A synthetic profile has no direct victim to raise an alert.
- Automation and AI tools. Criminals now use automated tools to generate identity details faster and build credit histories with less effort. These tools make the process scalable and low risk. A single person can create many synthetic identities and manage them quietly.
- Delayed detection and high cost. Synthetic identities take time to develop and they look safe for months or even years. No person reports a problem so banks discover the fraud only after the final hit.
How Synthetic Identity Criminals Gather and Build Data
Synthetic identity criminals collect information slowly and quietly. They do not need a full identity from one place. They gather small pieces from different sources and blend them into a new profile. Everything looks normal from the outside which makes synthetic identity fraud hard to spot until the fake identity is already strong.
- Leaked personal records. Criminals use data from large breaches that exposed Social Security numbers, phone numbers and other details. This information sits online for years and becomes raw material for building synthetic identities.
- Public and open sources. Some data comes from places that are not protected such as social media public records and old databases. Criminals collect names, birth dates and addresses without breaking into systems.
- Credit invisibles and thin files. Criminals look for people with little or no credit history like young adults, seniors or inactive users. These identities do not trigger alerts because no one actively monitors them which challenges synthetic identity fraud management teams.
- Fabricated documents and digital tricks. Criminals use tools to create fake IDs, utility records and digital proofs that look normal to basic checks. These documents support the synthetic identity during onboarding.
- Small activity to build trust. The synthetic identity opens a basic account and behaves like a regular customer. Bills are paid and the profile earns a clean record. After months of normal behaviour the criminal applies for higher credit.
The Major Challenges in Detecting Synthetic Identity Fraud
Detecting synthetic identity fraud is difficult because the fake identity does not look fake at the start. The system treats it like a real customer and nothing seems strange. This is why banks focus on how to stop synthetic identity fraud in banking through stronger verification, smarter monitoring and early pattern detection.
- Identity looks real in the beginning. A synthetic identity uses some real information so the first checks accept it without any pushback. The account opens and everything appears normal.
- No direct victim to warn anyone. Stolen identity fraud gets noticed when a real person reports a problem. Synthetic identity fraud does not work like that. No one feels harm at the start so no complaint is made.
- The profile builds trust over time. The synthetic identity behaves like a perfect customer for months. Bills are paid and activity looks clean. The system starts to trust the profile because nothing goes wrong.
- Old detection systems miss the pattern. Many organisations still use tools that look for stolen identities or known risky behaviour. Synthetic identities do not match those rules. The profile is new and clean. Nothing triggers the alert.
- Limited data sharing blocks visibility. One organisation may not see the bigger picture. Another bank may have the missing clue. Without shared insight it becomes hard to link patterns like repeated addresses or identifiers.
Business Impacts of Synthetic Identity Fraud
When fake identities slip into the system it’s not just a small leak it can cost businesses big money. Fraudsters build trust then cash out and the damage shows up in losses, reputation hits and higher security costs. Below are key stats to show how serious this is.
- Estimated losses of US $12 billion in 2024. Businesses worldwide lost about USD 12 billion to synthetic identity fraud in 2024 and the figure is projected to climb to at least USD 23 billion by 2030.
- 3.3 billion USD in exposure among US lenders H1 2025: According to a report, synthetic identities caused potential losses of USD 3.3 billion in the first half of 2025 among auto loans, bank credit cards, retail cards and unsecured personal loans in the U.S.
- Synthetic fraud may account for 80% of new account fraud. One industry paper estimates synthetic identity fraud accounts for up to 80 % of all new account fraud cases in some contexts.
- When 0.08% of government benefit transactions were synthetic, risk hit USD 283 million for one year. In one analysis of government-benefit filings, if just 0.08 % are synthetic, that alone could expose the agency to USD 283 million in a year.
Effective Techniques for Synthetic Identity Fraud Detection
Detecting synthetic identity fraud needs more than one clever tool. Strong defence comes from layers that examine data behaviour and context together. The goal is simple. Make life hard for fake identities and easy for real customers while keeping systems ready to spot synthetic identity fraud before it grows.
- Stronger identity proofing at onboarding. Real defence starts when a new customer first shows up. Businesses can check identity data against trusted sources and watch for small mismatches that do not fit a real life story.
- Device and behaviour intelligence. A synthetic identity might look fine on paper but the way it moves can feel wrong. Systems can watch device history login patterns and location habits to see if this so-called person behaves like a real human.
- Network and link analysis. Synthetic identities often live in groups. They may share phone numbers, addresses or devices in the background. Graph tools can connect these dots and show hidden clusters.
- Ongoing monitoring beyond onboarding. Detection is not a one time event. A synthetic identity may act perfect for months before a big move. Continuous monitoring of payments credit use and account changes helps spot sudden shifts in behaviour.
- Smarter AI and machine learning models. Machine learning can learn what normal looks like for real customers and highlight profiles that feel close to synthetic behaviour. These models look at hundreds of signals at once.
- Stronger KYC supported by biometrics. Better KYC is not only more documents. It is smarter verification. Liveness checks face match and behavioural biometrics add extra proof that a real person sits behind the application.
How to Prevent Synthetic Identity Fraud in 2026
In 2026 prevention means thinking about the full life of an identity. Not just the sign up moment. Criminals now use advanced tools to build fake people so businesses need smart layers that protect every step.
- Treat onboarding as your first strong gate. Do not rush every new account just to keep the journey fast. Use trusted data sources and document checks to see if the story of the person feels real.
- Follow modern digital identity standards. Use guidance like the latest NIST digital identity rules to shape your process. These standards push for stronger proofing, smarter authentication and better defence against spoofed documents and deepfake style tricks.
- Use document devices and behaviour together. A passport or ID can look fine on screen. So add extra views. Check the device history. Look at behaviour during the session. See how the person types and moves. These layers make it easier to spot synthetic identity fraud before it settles in.
- Add biometrics with strong liveness tests. Face match and other biometric tools can help only when they also prove there is a real living person in front of the camera. Liveness tests defend against masks screens and deepfake video.
- Bring in AI and graph analytics. Modern tools can scan patterns that humans miss. AI models can look across many signals and spot profiles that behave like synthetic identities.
- Monitor through the full customer life. Do not stop after onboarding. A synthetic identity often behaves well at first. Use ongoing monitoring to watch for sharp changes in spending new devices or risky updates.
- Share insight through trusted partners. No single bank or fintech sees everything. Join industry groups and trusted data sharing networks where you can learn about fresh fraud patterns.
- Strengthen internal teams and playbooks. Prevention is not just a tool problem. Train staff to understand how synthetic identities work and how they look in real cases. Build clear playbooks for review escalation and account blocking.
- Explain safety steps to customers. Good customers may feel confused when you ask for extra checks. Clear simple messages help them see that stronger identity proofing protects them too.
Real-World Synthetic Identity Fraud Examples
Synthetic identity fraud is not a theory. It is happening in real life across different sectors and countries. Criminals build fake identities that look completely normal inside the system. The fraud grows slowly and nobody notices anything wrong.
- Ontario ring creates hundreds of fake profiles. Police in Ontario uncovered a group that made over 680 synthetic identities starting in 2016. The fake profiles accessed credit unnoticed and caused millions in losses showing how long this fraud can hide.
- Life insurance faces a new synthetic fraud wave. The life insurance sector links synthetic identity fraud to about USD 30 billion in yearly losses and up to 85% of identity fraud cases.
- Synthetic identity fraud: How AI is changing the game. AI is helping fraudsters build synthetic identities that are hard to spot. Losses tied to this type of fraud rose from about USD 8 billion in 2020 and have climbed toward USD 30 billion over the past few years.
Strengthen Identity Security Posture with Infisign
Synthetic identity fraud grows when fake people slip through weak checks and stay inside for a long time. Infisign closes that gap from day one.
UniFed protects customer identities in one secure layer while the IAM Suite protects your workforce and partners. Both sit on one platform so you see every identity in one view and you control how each user logs in moves and gets access across your full stack.
Smart Multi-Factor Authentication
Infisign Smart MFA strengthens identity checks in a way that fits perfectly with synthetic-fraud defence. It adjusts the level of verification based on real-time risk so real customers move smoothly while suspicious activity gets extra barriers. This helps block synthetic identities across cloud apps, on-prem systems and hybrid setups without breaking the user experience.
Why Adaptive MFA From Infisign Stops Synthetic Identities Before They Settle In
- Infisign Adaptive MFA raises the trust level of every login by checking context, intent, and identity strength at the same time.
- It reads real time signals across devices and behaviour, and pairs them with biometrics and passwordless verification to shut down synthetic profiles that try to blend in.
Because it works with existing authenticators and legacy environments, teams upgrade security without slowing operations.
- Context aware checks measure location, device health, user role, and live behaviour to expose unnatural patterns
- High assurance methods like biometrics, passkeys, and passwordless login make it almost impossible for synthetic identities to bypass proof of presence.
- It supports biometrics, FIDO2 keys, WebAuthn, and time-based OTP for modern apps, while also offering push approvals and email or SMS codes as flexible verification options.
Passwordless authentication
Infisign’s passwordless flow use FIDO2 WebAuthn and device bound passkeys to reduce credential based fraud by removing shared secrets entirely. The private key stays secured on the user’s device, cannot be reused across sites, and cannot be captured or replayed by synthetic identities.
Continuous identity risk monitoring
Infisign does not stop after onboarding. It keeps watching logins and access in real time. Sudden changes in device, location, access time or resource use push the identity into higher risk. Conditional rules can step up MFA, block the attempt or send it for review. Synthetic identities that stay quiet then rush for a big hit are caught at this stage.
Identity governance and access control
Infisign gives each identity only the access it truly needs. Roles policies and conditional access make sure a fake low level user cannot quietly reach high value systems. Excess rights are removed fast. Access reviews and audit trails show who has what and why. Synthetic identities find fewer open doors to exploit.
Credential and identity lifecycle automation
Infisign automates how identities appear, change and leave. New users get the right access from day one. Leaving users lose access in a clean and complete way. Tenant management keeps each organisation and environment isolated. This reduces stale accounts and shadow access which synthetic identities love to copy or abuse.
Other Infisign strong security features
- Universal single sign on. Infisign Universal SSO gives users one secure entry point and goes live in about 4 hours. Built-in social logins like Google and Facebook keep access fast for real customers while reducing password risks that synthetic identities try to exploit.
- App integration platform. Infisign connects with more than 6000+ apps instantly and fits into your existing stack without extra development. Its APIs and SDKs keep integration simple and fast so there are fewer gaps for synthetic identities to use.
- Conditional access policies. Conditional access policies decide who can reach sensitive areas by checking real time risk signals. These policies react fast when they see unusual actions or risky downloads and either block the attempt or ask for stronger proof. Infisign builds these policies with location rules, device compliance checks, and real time risk evaluation.
- Login thresholds and IP throttling. Infisign limits repeated login attempts and slows or blocks strange traffic. This stops brute force attempts and scripted probes that test synthetic identities at scale.
- Directory sync and HRIS integration. All directories and HR systems stay in sync. When someone joins, moves or leaves the change flows to every app. That reduces ghost accounts that could turn into synthetic style profiles later.
- Impersonation control. Support teams can safely act as users for troubleshooting with full audit logs. Every action is recorded so this power cannot be abused to grow hidden synthetic identities.
- Automated user management. Provisioning and deprovisioning run through rules and AI. Tenants stay isolated and clean. This reduces manual mistakes and closes gaps where fake identities can hide.
- Compliance and auditing. Every login and access event is logged. Reports show regulators how you protect identities. Clear trails make it easier to trace and shut down synthetic identities when they appear.
- Non human identity protection. Infisign secures bots services and API keys with the same care as people. No passwords sit in scripts. Rules define what each machine can touch. Synthetic fraud that hides behind service accounts becomes harder.
- Network Access Gateway. On premise apps sit behind secure encrypted tunnels. Only verified identities can reach them. Synthetic users on the open internet cannot walk in.
- Zero Knowledge Authentication. Users prove who they are without exposing secrets. Attackers have nothing to steal and replay. This protects both real and synthetic targets from credential theft.
- MPWA and Password Vault. Legacy apps that still need passwords are wrapped inside secure automation. Users never see the secrets. Synthetic identities cannot reuse or share those credentials.
- AI Access Management. Infisign AI Access Management lets users request access with a simple message. The system reads the request checks policy and gives a real time decision in seconds. It also works inside Slack and Teams and sends sensitive requests to the right manager for quick approval.
- Privileged Access Management. Infisign gives admin rights only when a task requires it and removes them as soon as the work is done. Every privileged action is logged so activity stays visible. Least privilege is built in and external teams use just in time access instead of permanent rights. This reduces risk while keeping a full audit trail.
- Customer experience and data protection. Self service tools and clear consent flows keep accounts accurate and trusted. Strong verification supports clean customer records so it becomes harder for a synthetic profile to blend into your user base.
- Flexible deployment architecture. Infisign runs in public cloud private setups or hybrid models. You can place identity controls close to your data and apps. That gives you tighter security and less blind space for synthetic identities.
Synthetic identity fraud is rising fast. See how Infisign blocks it at every step. Click to try the demo and strengthen security today.
FAQs
How to identify synthetic fraud?
Look for identity details that do not match real records. Watch for new accounts with unusual behavior. Check shared phone numbers or addresses across users. Use monitoring and risk signals.
What are the four types of identity theft?
Financial identity theft uses personal data to open accounts. Medical identity theft uses health details. Criminal identity theft links a person to crimes. Synthetic identity theft builds a new identity.
Why is a synthetic identity so hard to detect?
Because part of the identity is real the system accepts it. No real victim reports the problem. The profile acts normal for months. Detection comes late after the fraud hits.
What are 5 ways to prevent identity theft?
Use strong authentication for every login. Check identity details against trusted sources. Monitor account activity often. Do not share sensitive data. Report strange behavior early to stop further fraud.
