Best Synthetic Fraud Detection Tools for Banks

Summary

Synthetic fraud is quickly becoming one of the most formidable challenges in B2B banking and fintech. As fraudsters leverage increasingly sophisticated tactics, merging real and fabricated data to create synthetic identities, banks and financial institutions must stay ahead with equally advanced countermeasures. Today’s leading synthetic fraud detection platforms harness the power of machine learning, behavioral analytics, and advanced orchestration to identify subtle anomalies, adapt in real time, and maintain regulatory compliance.

This guide provides a clear, data-driven comparison of top synthetic fraud detection tools, each tailored to the complex needs of modern banks. We examine their core capabilities, use cases, integration strengths, and recent innovations, empowering decision-makers to select solutions that not only mitigate fraud, but also streamline operations and support future growth. Whether your institution is focused on seamless onboarding, robust compliance, or combating emerging threats, the insights below will help you navigate a rapidly evolving landscape with confidence and clarity.

FraudNet

Platform Summary:
FraudNet is a comprehensive platform built for enterprise financial institutions to combat the growing threat of B2B synthetic fraud. By moving beyond traditional data point verification, FraudNet analyzes holistic behavioral patterns and hidden connections to expose synthetic identities, protecting banks from new account fraud, loan defaults, and credit bust-out schemes.

Key Benefits:

• Proactively identifies and neutralizes synthetic identity attacks at onboarding and throughout the customer lifecycle
• Leverages a global anti-fraud network for real-time threat intelligence
• Streamlines compliance and operational workflows with unified fraud, risk, and compliance management
• Minimizes friction for legitimate customers while maximizing fraud prevention

Core Features:

• Synthetic Identity Analysis: Cross-verifies hundreds of ID data points in real time, flagging inconsistencies and behavioral anomalies
• Machine Learning Models: Detects subtle, evolving fraud patterns invisible to human analysts
• Global Anti-Fraud Network: Taps into billions of anonymized data points for immediate identification of reused fraud components
• Entity Resolution & Account Chaining: Connects disparate accounts to unmask synthetic fraud rings and provide a unified risk view

Primary Use Cases:

• New Account & Loan Application Screening: Instantly analyzes onboarding data to prevent synthetic identities from entering the ecosystem
• Preventing Credit Bust-Out Schemes: Continuously monitors post-onboarding behavior to detect and intervene in bust-out fraud
• Identifying Money Mule Networks: Flags and blocks money muling activity by linking anomalous transaction patterns across accounts

Recent Updates:
FraudNet’s models are continuously updated to address new and emerging synthetic fraud threats. Recent enhancements include a no-code integration toolkit for ERP/AP systems, upgraded deepfake and synthetic identity detection models, and fully customizable compliance reporting tools. The platform’s architecture is designed for ongoing evolution, ensuring your institution’s defenses remain at the forefront of synthetic fraud prevention.

Setup Considerations:

• Seamless modular integration with existing banking platforms and core systems
• Scalable AWS-powered infrastructure to support high transaction volumes
• Flexible, customizable rule-setting for adaptive risk management
• Collaborative implementation and expert support for maximum ROI and protection

Eftsure

Platform Summary:
Eftsure is a specialized fraud prevention platform focused on real-time vendor validation and accounts payable (AP) fraud for mid-to-large B2B enterprises. Its crowd-sourced vendor database and multi-factor authentication help mitigate risks like payment redirection and business email compromise.

Core Features:

• Real-Time Vendor Validation using a proprietary, crowd-sourced database
• Centralized Audit Dashboard for streamlined compliance and audit preparation
• Enhanced ERP Integration for automated vendor validation within financial workflows

Primary Use Cases:

• Accounts Payable Fraud Prevention
• Automated Vendor Onboarding
• Audit Preparation and centralized compliance reporting

Recent Updates:
Eftsure has introduced deepfake fraud assessment tools, expanded its vendor validation database, improved real-time monitoring, and enhanced ERP integration to further automate financial workflows.

Setup Considerations:

• Limited customization of fraud detection rules
• Primarily focused on vendor and payment fraud, not broader synthetic identity risks
• Best suited for mid-to-large enterprises with AP-centric fraud prevention needs

Sift

Platform Summary:
Sift delivers behavioral analytics and real-time risk scoring for high-volume digital commerce and online marketplaces. Its global data network and flexible APIs make it a strong choice for organizations seeking advanced fraud detection in digital environments.

Core Features:

• Behavioral Analytics Engine for real-time detection of synthetic identities and account takeovers
• Modular API Integration for seamless deployment in diverse digital channels
• Global Data Network processing over a trillion events annually

Primary Use Cases:

• E-commerce Fraud Prevention
• Account Takeover Protection
• Marketplace Integrity and abuse prevention

Recent Updates:
Sift has enhanced its device intelligence, expanded behavioral analytics to new channels, and improved chargeback protection for high-volume merchants.

Setup Considerations:

• May be cost-prohibitive for smaller institutions
• Advanced customization and integration require technical resources
• Primarily optimized for digital commerce, less so for traditional banking workflows

Feedzai

Platform Summary:
Feedzai offers a unified RiskOps platform that integrates fraud detection, anti-money laundering (AML), and compliance management for large banks and fintechs. Its contextual risk scoring and built-in compliance tools support holistic risk management.

Core Features:

• Unified Fraud & AML Platform
• Contextual Risk Scoring using behavioral, device, and network-wide signals
• Built-In Compliance Reporting and audit trail management

Primary Use Cases:

• Banking Fraud and AML Monitoring
• Dynamic Customer Risk Profiling
• Automated Regulatory Reporting

Recent Updates:
Feedzai has strengthened its AML monitoring, broadened analytics for new fraud typologies, and improved orchestration for multi-channel financial services.

Setup Considerations:

• Resource-intensive implementation and integration
• Enterprise-level pricing
• Extensive customization and ongoing management required for optimal results

Kount

Platform Summary:
Kount provides rapid, accurate fraud prevention for e-commerce and digital-first banks through identity trust scoring and advanced device analytics. Its APIs and plug-ins support fast deployment and scalable controls for growing organizations.

Core Features:

• Identity Trust Scoring with hundreds of data points per interaction
• Device Analytics & APIs for fast deployment
• Real-Time Risk Assessment and customizable business policies

Primary Use Cases:

• E-commerce Fraud Prevention
• Device-Based Risk Assessment
• Scalable Controls for Mid-Market Sellers

Recent Updates:
Kount has improved device analytics, expanded velocity monitoring, and enhanced integration with payment gateways and digital wallets.

Setup Considerations:

• Limited AML/compliance features
• Higher pricing for small businesses
• Primarily designed for digital sellers, less so for traditional banking

SEON

Platform Summary:
SEON is an agile, API-first fraud detection platform designed for mid-market and digital-first banks. Its digital footprinting and customizable rule engine enable rapid, flexible deployment with minimal IT overhead.

Core Features:

• Digital Footprinting & Custom Rules using over 900 data points
• API-First, Rapid Deployment
• Customizable Rule Engine with transparent suggestions

Primary Use Cases:

• Mid-Market Fraud Detection
• Customizable Risk Controls
• Global Expansion for banks entering new markets

Recent Updates:
SEON has added new device intelligence signals, expanded rule engine customization, and improved onboarding for non-technical users.

Setup Considerations:

• Limited advanced analytics and compliance features
• Limited support for manual or hybrid decisioning workflows
• Best for mid-market banks; larger institutions may require more comprehensive features

What is B2B Synthetic Fraud Detection?

B2B synthetic fraud detection tools are specialized software solutions designed to identify and prevent fraudulent business entities from accessing financial services. Unlike traditional fraud targeting individuals, B2B synthetic fraud involves creating entirely fabricated or manipulated business identities. Fraudsters combine legitimate information, such as a real but inactive Employer Identification Number (EIN), with fictitious details like fake director names, sham websites, and non-existent physical addresses. The goal is to build a seemingly credible business profile that can pass initial due diligence, only to be used later to secure large commercial loans, lines of credit, or merchant accounts with the intent to default, an event known as a "bust-out."

Why is Synthetic Fraud Detection Important?

Proactive detection is critical because B2B synthetic entities are purpose-built to evade legacy fraud prevention systems. Traditional checks often focus on verifying individual data points against known fraud lists or credit histories, but synthetic identities have no negative history because they are brand new. These fraudulent businesses can operate for months or even years, building a veneer of legitimacy by making small transactions and payments, making them appear as low-risk clients. The financial and reputational damage from a successful bust-out can be catastrophic, resulting in multi-million dollar credit losses, regulatory scrutiny for inadequate KYC/KYB (Know Your Customer/Know Your Business) controls, and a compromised portfolio. For a deeper dive into the latest fraud prevention trends shaping the financial sector, explore our resource library.

How to Choose the Best Software Provider

Choosing the right provider requires evaluating their technological capabilities and data intelligence. Look for a solution that leverages a comprehensive data consortium, pulling from thousands of diverse public and private sources to create a holistic view of a business entity and its principals. The provider's platform must utilize advanced machine learning, specifically trained to detect the subtle anomalies and non-obvious patterns indicative of synthetic identities-something simple rules-based engines miss. Key features to demand include real-time risk scoring, sophisticated link analysis to uncover hidden relationships between entities and individuals, and seamless API integration for frictionless implementation into your existing onboarding and underwriting workflows. For a list of best fintech fraud prevention tools, see our curated recommendations.

Frequently Asked Questions

What is synthetic fraud, and why is it a growing concern for banks?

Synthetic fraud occurs when criminals combine real and fictitious information to create new, fake identities that appear legitimate to financial institutions. Unlike traditional identity theft, synthetic fraudsters often use a mix of real data (such as social security numbers) with fabricated details (like names or addresses) to open accounts, secure loans, or conduct fraudulent transactions. This type of fraud is particularly challenging for banks because synthetic identities can pass standard verification checks and remain undetected for long periods, leading to significant financial losses, regulatory penalties, and reputational damage. The increasing sophistication of fraud tactics and the availability of stolen data on the dark web have made synthetic fraud one of the fastest-growing threats in B2B banking.

How do synthetic fraud detection tools work?

These tools leverage advanced technologies such as machine learning, behavioral analytics, and big data to identify patterns and anomalies that may indicate fraudulent activity. Platforms analyze vast amounts of data in real time-including identity attributes, transaction histories, device fingerprints, and behavioral signals-to detect inconsistencies or suspicious behaviors that are difficult for human analysts to spot. Many solutions also use global threat intelligence networks to cross-reference data and identify reused fraud components. By continuously learning from new fraud trends and adapting their models, these tools help banks stay ahead of evolving threats and reduce false positives, ensuring legitimate customers are not inconvenienced. For more on how machine learning is transforming fraud detection, visit our guide to machine learning tools for fraud detection.

What are the key features to look for in a B2B synthetic fraud detection solution?

When evaluating synthetic fraud detection tools for banks, decision-makers should prioritize features such as real-time identity verification, advanced machine learning models, behavioral analytics, seamless integration with existing banking systems, and robust compliance reporting. Additional capabilities like global anti-fraud data networks, customizable rule engines, and entity resolution (to link related accounts and uncover fraud rings) can further enhance detection accuracy. It’s also important to consider scalability, ease of deployment, support for regulatory requirements, and the ability to minimize friction for legitimate users. Explore our list of essential fraud prevention tools for more insights.

How can synthetic fraud detection tools support regulatory compliance?

Many synthetic fraud detection platforms offer built-in compliance features, such as automated audit trails, customizable reporting, and support for Know Your Customer (KYC) and Anti-Money Laundering (AML) requirements. By providing accurate, real-time risk assessments and documenting all fraud prevention activities, these tools help banks demonstrate due diligence to regulators and auditors. Some solutions also offer centralized dashboards for compliance officers and internal auditors, making it easier to monitor, investigate, and report on suspicious activities as required by law. For more on compliance-focused solutions, see our overview of compliance technology.

What are the main challenges in implementing synthetic fraud detection solutions in banks?

Implementing synthetic fraud detection tools in banking environments can present challenges such as integration with legacy systems, managing large volumes of data, balancing fraud prevention with customer experience, and ensuring ongoing model accuracy as fraud tactics evolve. Additionally, resource constraints, change management, and the need for specialized expertise can impact deployment timelines. To overcome these challenges, banks should seek solutions with modular integration options, strong vendor support, scalability, and flexible rule-setting capabilities tailored to their specific risk profiles and operational workflows. For strategies to detect and prevent new account fraud, review our guide to new account fraud prevention.

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.