Top 10 AI Tools Helping Banks Detect Synthetic Identity Fraud
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 problem synthetic identity fraud creates for banks. Rather than simply impersonating one victim, criminals combine genuine personal information with invented names, addresses, phone numbers or dates of birth to construct 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.
The scheme can remain hidden for months or years. Fraudsters may make small payments, build a credible credit history, and gradually qualify for larger limits before maxing out every available account and disappearing. Because no conventional identity-theft victim immediately complains, the resulting loss may be written off as ordinary bad debt.
The financial exposure is rising quickly. 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 produce at least $23 billion in losses by 2030. Generative AI is making the threat harder to contain by helping criminals create forged documents, realistic profile photographs, deepfake videos and thousands of consistent fictional personas.
No single product can solve every part of this problem. Banks increasingly need several layers working together, including identity graphs, behavioral biometrics, device intelligence, document analysis, network connections and continuous transaction monitoring. The following platforms stand out for their ability to bring those signals into real-time fraud decisions.
Socure Sigma Synthetic Fraud
Socure offers one of the most purpose-built products in this market through Sigma Synthetic Fraud. Rather than treating every applicant with a limited credit history as suspicious, the model is designed to separate legitimate thin-file or new-to-country consumers from manipulated and fabricated identities. It analyzes relationships between names, addresses, phone numbers, email accounts, devices, IP addresses, velocity patterns, and wider online and offline identity records.
Its latest model is used by all five of the five largest US banks, alongside 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 makes it especially useful for banks that want a synthetic-specific risk score without creating unnecessary friction for young consumers or applicants with little conventional credit history.
SentiLink Synthetic Fraud Score
SentiLink has built its reputation around detecting synthetic identities at the application stage. Its Synthetic Fraud Score estimates the probability that the name, date of birth and Social Security number presented by an applicant do not belong to one cohesive person. The company distinguishes between manipulated identities, where 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 understand problems such as suspicious Social Security number relationships, unusual phone histories or mismatched identity elements. SentiLink’s fraud report covering the second half of 2025 was based on more than 236 million account-opening applications, while 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 inside the records of one institution.
Alloy Fraud Platform
Alloy approaches synthetic identity detection as a decision-orchestration problem. Banks often purchase identity data, document checks, device tools and fraud scores from multiple vendors, only to discover that the systems do not communicate effectively. Alloy brings those signals into 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 only when an application crosses a defined threshold. For synthetic identity detection, that flexibility matters because the warning sign is rarely one obviously fake document. It is more often a collection of small inconsistencies spread across contact information, devices, addresses and application histories. Alloy helps banks turn those scattered clues into one coordinated decision.
Sardine
Sardine combines identity verification with device intelligence, behavioral biometrics, consortium data and graph analytics. Its technology can look beyond the information typed into an application and examine how the application was completed, whether a device has appeared elsewhere in the network and whether several supposedly unrelated identities share hidden technical connections. Sardine’s Connections Graph is particularly relevant to banks facing organized synthetic fraud because it can reveal 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 may miss. Sardine also carries risk signals beyond onboarding into transaction monitoring and anti-money-laundering workflows. That lifecycle view helps banks identify synthetic accounts that initially behave well 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 is important 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 rather than 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, illustrating how central automated analysis has become to modern bank defenses.
TransUnion TruValidate Synthetic Fraud Model
TransUnion’s TruValidate Synthetic Fraud Model benefits from the company’s position at the intersection of credit histories, identity records and digital risk intelligence. The product is purpose-built 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.
That allows a bank to ask not only whether a Social Security number exists, but whether the complete identity has 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. Its technology can therefore support both initial screening and portfolio monitoring, helping lenders detect identities that become suspicious only after credit has already been extended.
LexisNexis Fraud Intelligence Synthetic Score
LexisNexis Risk Solutions uses extensive public, proprietary and cross-industry information to identify identity combinations that do not make sense when viewed as a whole. Its Fraud Intelligence Synthetic Score evaluates more than 170 identity characteristics and life events, looking for inconsistencies involving Social Security numbers, names, email addresses, telephone numbers and other attributes. The result is delivered as a three-digit risk score accompanied by ranked warning codes, giving investigators more context than a simple approve-or-decline response.
According to LexisNexis, the model can detect signs such as unusually recent credit-bureau existence, overcrowded contact fields and identity elements that have appeared at suspicious velocity. Because the score is available through an API, batch processing or the company’s decision platform, banks can insert 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 at the moment 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 a strong 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 movement, touchscreen activity, navigation habits, device characteristics and signs of automated or remotely controlled sessions. This gives banks another line of defense when a synthetic identity arrives with 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 across the customer journey. BioCatch says its technology is used by more than 350 retail banks and analyzed billions of user sessions each month. Although behavioral biometrics may not prove that an identity exists on its own, it can expose 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 using data validation, document authentication, biometrics, risk intelligence and anti-money-laundering screening. Banks can configure different verification journeys according to 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 during 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, reinforcing the need to move from one-time identity checks to continuous assurance.
Banks Need Layers, Not a Single Score
The best platform for a bank depends on where its current defenses are weakest. A lender struggling to distinguish thin-file consumers from fabricated identities may prioritize Socure, SentiLink, TransUnion or LexisNexis. An institution with too many disconnected vendors may gain 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, while Experian and Mitek provide powerful combinations of identity data, document analysis and step-up verification.
The broader lesson is that synthetic identity fraud cannot be defeated 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 who was manufactured by a fraud ring.
Banks must determine whether an identity has a believable history, whether its digital behavior looks 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 the ability to connect those clues before a fictional customer becomes a very real loss.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
