A customer submits a valid Social Security number, passes a database check and presents a convincing identity document. Everything appears normal—except the person applying for the account does not actually exist.

That is the challenge synthetic identity fraud poses to banks. Instead of impersonating a single victim, criminals combine genuine personal information with invented names, addresses, phone numbers or dates of birth to create a new identity. The Federal Reserve defines the crime as using a combination of personally identifiable information to fabricate a person or entity for dishonest or financial gain.

Synthetic identity schemes can remain concealed for months or years. Fraudsters may make small payments, establish a credible credit history and gradually qualify for higher limits before maxing out available accounts and disappearing. Because no conventional identity-theft victim immediately reports the crime, the resulting loss may be recorded as ordinary bad debt.

The financial exposure is increasing. TransUnion estimated that synthetic identity exposure across open US credit-card, retail-card, auto-loan and personal-loan accounts reached $3.3 billion at the end of 2024. Deloitte expects synthetic identity fraud to generate at least $23 billion in losses by 2030.

Generative AI is making the threat more difficult to contain by helping criminals produce forged documents, realistic profile photographs, deepfake videos and thousands of consistent fictional personas. No single product can address every aspect of the problem. Banks increasingly need multiple layers working together, including identity graphs, behavioral biometrics, device intelligence, document analysis, network connections and continuous transaction monitoring.

The following platforms are notable for their ability to combine those signals in real-time fraud decisions.

Socure Sigma Synthetic Fraud

Socure’s Sigma Synthetic Fraud is one of the market’s purpose-built products. Rather than treating every applicant with a limited credit history as suspicious, the model is designed to distinguish legitimate thin-file or new-to-country consumers from manipulated and fabricated identities.

It analyzes relationships among names, addresses, phone numbers, email accounts, devices, IP addresses, velocity patterns and broader online and offline identity records. Socure says its latest model is used by all five of the five largest US banks, as well as major card issuers and hundreds of fintech companies.

The platform can also be combined with document verification, behavioral analytics and device intelligence through Socure’s broader identity stack. This can help banks obtain a synthetic-specific risk score without creating unnecessary friction for young consumers or applicants with limited conventional credit histories.

SentiLink Synthetic Fraud Score

SentiLink has built its reputation around detecting synthetic identities during the application process. Its Synthetic Fraud Score estimates the likelihood that an applicant’s name, date of birth and Social Security number do not belong to one cohesive person.

The company distinguishes between manipulated identities, in which someone may use their real name with another person’s identification number, and fully fabricated identities assembled by organized fraud rings. Machine-learning scores are supplemented by explainable flags that help investigators examine issues such as suspicious Social Security number relationships, unusual phone histories and mismatched identity elements.

SentiLink’s fraud report covering the second half of 2025 was based on more than 236 million account-opening applications. The company said it served eleven of the fifteen largest US banks. That scale gives its models visibility into coordinated attacks that may appear insignificant in the records of a single institution.

Alloy Fraud Platform

Alloy approaches synthetic identity detection as a decision-orchestration problem. Banks often obtain identity data, document checks, device tools and fraud scores from multiple vendors, only to find that the systems do not communicate effectively. Alloy brings those signals together in configurable onboarding and account-monitoring workflows.

Its Fraud Signal uses machine learning trained on aggregated behavior and risk patterns across a network of more than 900 financial institutions and fintech companies. The platform can combine information from hundreds of data services, apply a bank’s own risk appetite and trigger stronger verification when an application crosses a defined threshold.

That flexibility is important in synthetic identity detection because the warning sign is rarely one obviously fraudulent document. More often, it is a collection of small inconsistencies involving contact information, devices, addresses and application histories. Alloy is designed to turn those scattered clues into a coordinated decision.

Sardine

Sardine combines identity verification with device intelligence, behavioral biometrics, consortium data and graph analytics. Its technology can examine more than the information entered into an application, including how the application was completed, whether a device has appeared elsewhere in the network and whether supposedly unrelated identities share hidden technical connections.

Sardine’s Connections Graph is particularly relevant to organized synthetic fraud because it can identify clusters of accounts linked through common devices, merchants, addresses or transaction patterns. Its Graph Analyst agent is designed to investigate those relationships and surface coordinated abuse that a conventional point solution might miss.

Sardine also extends risk signals beyond onboarding into transaction-monitoring and anti-money-laundering workflows. That lifecycle view can help banks identify synthetic accounts that initially behave normally but later begin receiving suspicious transfers, moving money rapidly or operating as part of a mule network.

Feedzai

Feedzai connects identity screening at account opening with ongoing payment and behavioral monitoring. Its Secure Onboarding capability can orchestrate different risk signals through one interface and retain the resulting risk profile after an applicant has been approved.

That continuity matters because a synthetic identity may pass initial verification and spend months building a credible history before attempting a bust-out. Feedzai can monitor device characteristics, behavioral changes and transactions throughout that period instead of treating onboarding as the final identity decision.

The company says its identity products are designed to prevent synthetic fraud, impersonation and account takeover across the customer lifecycle. Feedzai also reported in its global survey of financial-crime professionals that 90% of participating financial institutions were already using AI to accelerate investigations or detect new fraud tactics. The finding illustrates the growing role of automated analysis in modern bank defenses.

TransUnion TruValidate Synthetic Fraud Model

TransUnion’s TruValidate Synthetic Fraud Model draws on the company’s position at the intersection of credit histories, identity records and digital risk intelligence. The product is designed to identify synthetic identities while limiting the rejection of legitimate applicants.

TruValidate can combine offline identity information with browsing footprints, phone-network data, device identity and other digital signals through an identity graph. This allows a bank to assess not only whether a Social Security number exists, but also whether the complete identity appears to have developed naturally over time.

TransUnion’s research found that US lender exposure linked to synthetic identities reached a record level, with open-account exposure rising to approximately $3.3 billion by the end of 2024. The technology can support both initial screening and portfolio monitoring, helping lenders identify identities that become suspicious only after credit has been extended.

LexisNexis Fraud Intelligence Synthetic Score

LexisNexis Risk Solutions uses public, proprietary and cross-industry information to identify combinations of identity elements that do not make sense when viewed together. Its Fraud Intelligence Synthetic Score evaluates more than 170 identity characteristics and life events, searching for inconsistencies involving Social Security numbers, names, email addresses, telephone numbers and other attributes.

The result is delivered as a three-digit risk score with ranked warning codes, providing investigators with more context than a simple approve-or-decline response. According to LexisNexis, the model can detect indicators such as unusually recent credit-bureau existence, overcrowded contact fields and identity elements appearing at suspicious velocity.

The score is available through an API, batch processing or the company’s decision platform. Banks can therefore integrate it into existing onboarding systems without rebuilding their entire fraud infrastructure.

Experian Precise ID

Experian’s Precise ID combines identity verification, fraud analytics and step-up authentication when an account is opened. Its models are designed to detect identity theft, first-payment default, bust-out behavior and synthetic identity fraud while helping banks approve genuine customers automatically.

Experian draws on cross-lender credit inquiries, demographic information and an identity graph showing how personal details have been connected and reused over time. Those links can reveal that one telephone number, address or Social Security number has appeared across multiple identities or applications.

Experian says nine of the ten largest US banks use Precise ID services and that the platform processes hundreds of millions of transactions annually. Banks can deploy the product directly or access it through Experian’s Ascend and CrossCore environments, making it an option for institutions that already rely on Experian data for credit and customer decisioning.

BioCatch Connect

BioCatch looks for evidence of fraud in the way a person interacts with a banking application. Its behavioral intelligence can analyze typing patterns, mouse movements, touchscreen activity, navigation habits, device characteristics and signs of automated or remotely controlled sessions.

This provides another line of defense when a synthetic identity is supported by documents and personal information that appear technically valid. BioCatch Connect combines real-time telemetry with predictive intelligence and thousands of application, network, device and transaction signals.

The platform is designed to detect account-opening fraud, bot activity, account takeover and mule accounts throughout the customer journey. BioCatch says its technology is used by more than 350 retail banks and analyzes billions of user sessions each month.

Behavioral biometrics may not independently prove that an identity exists. However, they can reveal when the applicant behind a polished identity package behaves like a bot operator, fraud farm or repeat criminal.

Mitek Verified Identity Platform

Mitek’s Verified Identity Platform, commonly known as MiVIP, focuses on layered identity assurance through data validation, document authentication, biometrics, risk intelligence and anti-money-laundering screening. Banks can configure different verification journeys based on the product, customer and level of risk involved.

Its AI-driven capabilities are intended to identify document manipulation, deepfakes and other synthetic content increasingly used in remote onboarding. Mitek also supports continued authentication after an account is opened, allowing banks to reuse verified biometric information for login or transaction approval.

Research released by Mitek and Datos Insights in 2026 found that 84% of surveyed fraud executives considered synthetic identity fraud a high or moderate application risk. The same research estimated US unsecured credit losses associated with synthetic identities at approximately $2.94 billion in 2025. The findings reinforce the need to move from one-time identity checks to continuous assurance.

Banks Need Layers, Not a Single Score

The most suitable platform for a bank depends on where its existing defenses are weakest. A lender struggling to distinguish thin-file consumers from fabricated identities may prioritize Socure, SentiLink, TransUnion or LexisNexis. An institution dealing with too many disconnected vendors may benefit more from Alloy’s orchestration model.

Banks facing bot farms, coordinated account clusters or synthetic mule networks may place greater emphasis on Sardine, Feedzai or BioCatch. Experian and Mitek provide combinations of identity data, document analysis and step-up verification.

The broader lesson is that synthetic identity fraud cannot be addressed by checking whether individual pieces of information are valid. A real Social Security number, deliverable address and convincing selfie can still belong to a person manufactured by a fraud ring.

Banks must assess whether an identity has a believable history, whether its digital behavior appears human, whether its component parts have suspicious relationships and whether its activity remains consistent after onboarding. AI is making synthetic identities easier to create, but it is also giving financial institutions tools to connect those clues before a fictional customer becomes a real loss.