Best Synthetic Identity Fraud Detection Software

Summary

Synthetic identity fraud is now one of the most persistent threats facing digital businesses, financial institutions, and e-commerce platforms. As fraudsters blend real and fabricated data to create convincing new personas, organizations need advanced solutions that go beyond traditional verification. This guide compares the leading synthetic identity fraud detection tools, highlighting their unique strengths, recent updates, and practical considerations to help you select the right platform for your needs.

Today’s top platforms offer more than just rule-based controls-they provide adaptive analytics, real-time intelligence sharing, and seamless integration with existing systems. By understanding the capabilities and differentiators of each solution, decision-makers can confidently navigate the complexities of synthetic identity fraud and safeguard both their organization and customers from evolving risks.

In the following sections, we provide an objective, structured comparison of the best synthetic identity fraud detection tools available in 2024, empowering you to make informed choices for your specific business context. For a broader look at the best synthetic identity detection tools and related resources, explore our curated guides.

1. Fraud.net

Platform Summary:
Fraud.net delivers a comprehensive ecosystem specifically designed to combat the complexities of synthetic identity fraud. Leveraging collective intelligence and advanced analytics, Fraud.net provides a holistic view of entity risk, enabling organizations to proactively identify and neutralize synthetic identity threats at the point of application and throughout the customer lifecycle. This platform is ideal for enterprises seeking to protect revenue and ensure operational integrity without compromising the experience for legitimate customers.

Key Benefits:

• Harnesses a global anti-fraud network for collective intelligence and early detection
• Employs advanced machine learning to analyze hundreds of data points in real time
• Offers multi-layered identity verification, making it nearly impossible for synthetics to pass undetected
• Seamlessly integrates with existing enterprise systems for scalable, unified fraud prevention

Core Features:

• Access to over one billion digital identities and trillions of risk assessments for unparalleled pattern recognition
• Real-time analysis of new account applications using device, network, and behavioral biometrics
• Cross-references email, phone, address, IP, and sanctions/watchlists for robust authentication
• Unified data orchestration that integrates third-party data and tools into a single hub for streamlined onboarding and compliance

Primary Use Cases:

• Secure new account onboarding for financial institutions, preventing synthetic checking, credit, and loan fraud
• Automated vendor and merchant screening to block synthetic business entities from infiltrating supply chains
• Protection for digital lending and BNPL platforms against synthetic identities attempting to build fraudulent credit histories

Recent Updates:
In 2024, Fraud.net launched Entity Screening to automate business verification and Policy Monitoring to proactively manage merchant compliance. The platform was also recognized with the Datos Insights Award for its unified AML and fraud transaction monitoring solution, underscoring its leadership in enterprise-grade financial crime prevention.

Setup Considerations:

• Collaborative onboarding with expert support to align the platform with your unique risk environment
• Flexible, scalable integration via a single API and marketplace for unifying existing tools
• Custom rule and model development for industry-specific behaviors and risk tolerance

2. Horizon Identity (ShadowDragon)

Platform Summary:
Horizon Identity by ShadowDragon is built for investigative teams that require deep identity resolution and open-source intelligence (OSINT) integration. The platform correlates emails, phone numbers, and usernames across hundreds of public sources and breach records, providing rich attribution and context for fraud investigations.

Core Features:

• Deep identity resolution across 550+ public sources and 15 billion breach records
• Dynamic profile expansion and noise filtering for focused, accurate investigations
• OSINT integration with Horizon Monitor and SocialNet for real-time monitoring and advanced link analysis

Primary Use Cases:

• Attribution and synthetic identity investigation for uncovering fraud rings
• Link analysis and network mapping to visualize connections between synthetic identities
• Compliance and case management with exportable records and audit trails

Recent Updates:
Recent improvements include enhanced integration with Horizon Monitor and SocialNet, enabling more advanced link analysis, as well as improved profile expansion and noise filtering for faster, more accurate investigations.

Setup Considerations:

• Most effective as a complement to core transaction monitoring systems
• Requires investigator expertise in OSINT and digital investigations
• Not designed for real-time transaction monitoring, so immediate fraud events may be missed

3. LexisNexis ThreatMetrix

Platform Summary:
LexisNexis ThreatMetrix is a leading solution for real-time identity and device risk analytics. It leverages a global, crowdsourced digital identity network to aggregate intelligence from billions of transactions, enhancing detection accuracy and supporting multi-channel fraud prevention.

Core Features:

• Crowdsourced digital identity network for broad, real-time intelligence
• SmartID device fingerprinting and behavioral biometrics for robust risk scoring
• Explainable scoring with SHAP-based explanations for transparent decisioning

Primary Use Cases:

• Real-time identity and device risk analytics across payments, onboarding, and account management
• End-to-end detection, investigation, and reporting with integrated workflow tools
• Workflow automation for case management and regulatory reporting

Recent Updates:
Recent updates include enhanced explainability with SHAP values, expanded device fingerprinting capabilities, and new no-code, self-service configuration tools to accelerate deployment and rule customization.

Setup Considerations:

• Commercial pricing may be less accessible for smaller organizations
• Detection accuracy improves with broader network participation
• Customizing detection rules and policies may require specialized expertise

4. Kount

Platform Summary:
Kount is a robust fraud prevention platform focused on e-commerce and payment environments. It employs dual-mode machine learning to adapt to both established and emerging fraud patterns, offering real-time risk scoring and integrated chargeback management tools.

Core Features:

• Dual-mode machine learning (supervised and unsupervised) for adaptive fraud detection
• Real-time risk scoring analyzing hundreds of transaction signals
• Chargeback and dispute management tools for streamlined operations

Primary Use Cases:

• E-commerce and payment fraud prevention
• Account takeover and loyalty fraud detection
• Custom policy enforcement for tailored fraud response

Recent Updates:
The latest updates include enhancements to fraud engines for quicker adaptation to new tactics, expanded integrations with leading e-commerce platforms, and improved dashboards for more streamlined investigations.

Setup Considerations:

• Primarily focused on e-commerce and payment fraud
• Requires regular tuning of business rules and models
• Relies mainly on internal and network data, with less use of external intelligence

5. Resistant AI

Platform Summary:
Resistant AI specializes in layered document and transaction forensics, applying over 500 forensic checks to financial documents to detect synthetic onboarding and document-based fraud. It integrates with existing fraud monitoring systems to enhance continuous KYC and compliance.

Core Features:

• Layered document and transaction forensics for onboarding and document fraud
• Persistent KYC and identity monitoring
• Real-time evaluation and improved explainability for analyst decision-making

Primary Use Cases:

• Onboarding and synthetic identity detection
• Transaction monitoring enhancement
• Continuous KYC and compliance monitoring

Recent Updates:
Recent improvements include expanded forensic analysis vectors, faster real-time evaluation, and enhanced model explainability to support analyst workflows in high-volume environments.

Setup Considerations:

• Most effective for organizations with significant document-based onboarding or KYC workflows
• Functions best as an overlay to existing fraud detection platforms
• Not a full replacement for core payment or account fraud monitoring tools

6. Socure

Platform Summary:
Socure is a platform that leverages a massive consortium network and advanced analytics to deliver industry-leading accuracy in synthetic identity fraud detection. It is designed for large-scale, regulated organizations across banking, fintech, e-commerce, and the public sector.

Core Features:

• Advanced machine learning trained on 400+ third-party data sources
• Consortium network and feature-level intelligence with 80+ predictive signals
• Automated controls management and compliance features including portfolio scrub and global watchlist screening

Primary Use Cases:

• Proactive identity fraud detection before onboarding
• Reducing false positives and operational costs through automation
• Scalable, real-time fraud prevention for regulated industries

Recent Updates:
Recent updates feature an expanded consortium network, an additional 150 million outcomes in the identity database, and improved feature-level intelligence for greater risk scoring accuracy.

Setup Considerations:

• Consortium-based approach may require data sharing
• Pricing and full feature set may be more accessible to larger enterprises
• Implementation may require integration with existing onboarding and risk management workflows

What is Synthetic Identity Fraud Detection?

Synthetic identity fraud detection refers to the specialized processes and technologies used to identify and prevent fraud committed using fabricated identities. Unlike traditional identity theft where a fraudster steals a real person's entire identity, synthetic identities are created by combining real information (like a valid Social Security Number) with fictitious details (like a made-up name and date of birth). Effective detection systems go beyond simple identity verification, employing advanced analytics and cross-referencing vast datasets to spot the subtle anomalies and illogical connections that are hallmarks of a synthetic profile before it can be used to establish credit and cause financial harm. For organizations seeking a comprehensive approach, entity risk management solutions can provide additional layers of protection.

Why is Synthetic Identity Fraud Detection Important?

This form of fraud poses a significant and growing threat to financial institutions, lenders, and other businesses because it is notoriously difficult to detect. Synthetic identities can appear legitimate for months or even years, building credit history before "busting out" by maxing out credit lines and disappearing, leaving behind substantial losses that are often written off as bad debt. Without a dedicated detection strategy, your organization is vulnerable to significant credit losses, skewed risk modeling, and increased operational costs associated with investigating these "ghost" accounts. Proactive detection is crucial for protecting your bottom line and maintaining the integrity of your customer portfolio. For a deeper dive into identity fraud protection strategies, explore our expert resources.

How to Choose the Best Software Provider

Selecting the right provider requires evaluating their methodology and technological capabilities. The best solutions utilize a multi-layered approach that doesn't rely on a single data source. Look for a provider that leverages a consortium of data, including credit bureau files, government records, utility data, and digital intelligence from device and behavioral signals. Critically, their platform should be powered by advanced models trained specifically to recognize the unique patterns of synthetic identity creation, such as the velocity of applications or inconsistencies in identity element history. Prioritize providers that offer real-time risk scoring, seamless API integration, and a clear ability to reduce false positives, ensuring you can stop fraudsters without adding friction for legitimate customers. For organizations in the fintech sector, our guide to the best fintech fraud prevention tools can help narrow your search.

Frequently Asked Questions

What is synthetic identity fraud and how does it differ from traditional identity theft?

Synthetic identity fraud occurs when fraudsters combine real and fictitious information-such as a legitimate Social Security number with a fake name, address, or date of birth-to create a new, fake identity. Unlike traditional identity theft, which involves stealing and using a real person’s complete identity, synthetic identity fraud creates entirely new personas that are not tied to a single, real individual. This makes detection more challenging, as there is often no direct victim to report suspicious activity.

How do synthetic identity fraud detection platforms use machine learning to identify threats?

Modern synthetic identity fraud detection platforms analyze vast amounts of data in real time. These technologies identify patterns, anomalies, and inconsistencies across multiple data points-such as device fingerprints, behavioral biometrics, and third-party data sources. By continuously learning from new fraud attempts and adapting to emerging tactics, these solutions can detect subtle indicators of synthetic identities that traditional rule-based systems may miss. For more on advanced analytics, see our overview of advanced analytics for fraud detection.

What should organizations consider when selecting a synthetic identity fraud detection solution?

When choosing a synthetic identity fraud detection platform, organizations should evaluate several factors: the breadth and depth of analytical capabilities, ease of integration with existing systems, scalability, compliance features, industry focus, and the ability to orchestrate data from multiple sources. It’s also important to consider the vendor’s track record, support resources, transparency of risk scoring, and whether the solution aligns with the organization’s specific risk environment and regulatory requirements. For a comparison of top solutions, visit our guide to detecting mule accounts.

Can synthetic identity fraud detection tools help with regulatory compliance?

Yes, many leading synthetic identity fraud detection platforms offer features that support regulatory compliance, such as automated investigations, audit trails, regulatory reporting, and persistent KYC (Know Your Customer) monitoring. These tools can help organizations meet requirements for anti-money laundering (AML), customer due diligence (CDD), and other financial regulations by providing robust identity verification and transparent decision-making processes. Explore our compliance solutions for more information.

How do these platforms integrate with existing fraud prevention and risk management systems?

Most advanced synthetic identity fraud detection solutions are designed for seamless integration with existing fraud prevention, onboarding, and risk management systems. Integration is typically achieved through APIs, unified data hubs, or marketplaces that allow organizations to connect third-party tools and data sources. This enables organizations to enhance their current workflows, unify case management, and orchestrate data for a more comprehensive approach to fraud detection and compliance. Learn more about case management technology for fraud operations.

Disclaimer: This article is based exclusively on publicly available information. The tools referenced have not been independently tested by us. Should you identify any inaccuracies or wish to provide recommendations, we invite you to contact us.