Best Fraud Detection Software in 2026

Updated: October 6, 2026

In a world where online transactions and personal data exchanges happen constantly, the threat of fraud is more prevalent than ever, and having the right fraud detection software has become essential.

Choosing the right fraud detection software is crucial for any financial institution. Whether it’s card fraud, identity theft, account takeover or scams, the consequences can be severe, causing both financial losses and lasting damage to your reputation. With so many options available, finding the right solution can feel overwhelming.

That’s why we’ve put together this guide to some of the best fraud prevention software on the market today. We’ll highlight their key features and why they stand out. Whether you’re a small fintech or part of a large bank, these tools can help protect your bottom line and keep your data secure.

Main factors to consider

Choosing the best fraud detection software depends on several criteria that align with your business needs and the level of security required.

First, consider the type of fraud you’re most concerned about, whether it’s card fraud, account takeover, identity theft or authorised push payment (APP) scams, as different solutions specialise in different threats.

Next, assess how easily the software integrates with your existing systems and processes. Scalability is also important: as your business grows, the software should handle increasing transaction volumes without compromising performance. Another critical factor is real-time detection and response, allowing swift action before fraud causes significant damage.

Keep in mind that attacks are getting faster. According to Signifyd’s 2026 State of Fraud report, e-commerce fraud pressure rose 33% year over year in early 2026, as AI makes attacks cheaper to run.

Also check how a solution handles AI-driven attacks, such as bots, deepfakes and AI agents acting on a customer’s behalf, and whether its AI decisions are explainable to your analysts and auditors.

For payment service providers in the EU, regulation matters too. PSD3 and the new Payment Services Regulation (PSR), expected to apply from 2027, put more focus on fraud prevention, fraud data sharing and impersonation scams. Fraud and AML teams increasingly work from shared data, so check whether fraud signals can also feed your AML monitoring.

Finally, look at cost-effectiveness: the software should offer a balance between price and the level of protection it provides.

AMLYZE platform

Before diving into the list, let’s look at how AMLYZE can support your organisation in the fight against financial fraud.

AMLYZE is primarily designed for AML/CFT (anti-money laundering and countering the financing of terrorism), but fraud is deeply intertwined with these domains. Money laundering frequently involves the proceeds of fraud, such as phishing, social engineering or investment scams. Terrorist financing can be underpinned by fraudulent transactions. And many transaction monitoring alerts are actually triggered by fraud-like behaviour.

Although AMLYZE is not a purpose-built fraud suite like those focused solely on card fraud or bot activity, it plays a critical role in detecting, classifying and reporting fraud-related activity within a broader financial crime context. The platform connects transaction monitoring with fraud monitoring, customer risk assessment, screening and investigations, so fraud and AML alerts are handled in one place.

Our rule engine and library of 200+ predefined scenarios include typologies that monitor high-risk merchant categories and locations, unusual or inconsistent spending patterns, use of multiple cards or accounts, chargeback and refund abuse, and cash withdrawal anomalies. Real-time rules can also act on fraud signals in payment data, such as repeated rejected payments within a short period or the IP address used for an operation.

In practice, this lets institutions proactively detect and respond to fraud, even when the primary use case is regulatory compliance or transaction screening.

Beyond AMLYZE, here are other carefully selected fraud detection solutions that can strengthen your organisation’s defence against financial crime.

LexisNexis Risk Solutions

LexisNexis Risk Solutions, part of RELX, is a leading provider of fraud detection and risk management solutions for banking, insurance and retail. Its ThreatMetrix solution, powered by the Digital Identity Network, links devices, locations and behaviour across billions of transactions a year to separate genuine customers from bad actors. In February 2025 it acquired IDVerse, adding AI-powered document authentication and deepfake detection.

Key features of LexisNexis Risk Solutions:

• Digital identity intelligence: device, location and behavioural signals shared across a global, cross-industry network.
• Identity verification and document authentication, including detection of deepfakes and AI-generated documents.
• Real-time fraud detection that stops fraud before it affects your bottom line.
• Customisable rules tailored to the challenges of different industries.

SEON

SEON is a fraud prevention platform trusted by financial institutions, e-commerce businesses and fintechs. Founded in Budapest and now headquartered in Austin, Texas, SEON raised an $80 million Series C in 2025 and launched an AI-driven AML suite, so fraud and compliance teams can work from one platform.

Key features of SEON:

• Digital footprint analysis: email, phone and IP signals combined with device fingerprinting to build a risk profile.
• Real-time risk scoring for each user and transaction.
• Customisable rules combined with machine learning, adapted to the needs of different industries.
• Fraud and AML checks in one platform.

Sardine

Sardine is an agentic AI risk platform for fraud, compliance and credit risk, used by banks, fintechs and payment providers. It raised a $70 million Series C in February 2025 and a $25 million extension led by National Bank of Canada in May 2026, bringing total funding to $170 million. In September 2026 it launched Sardine AI Labs to build foundation models for fraud detection.

Key features of Sardine:

• Device and behavioural intelligence to detect fraud as it happens.
• Real-time risk scoring based on large volumes of transaction data.
• AI agents that help analysts review and resolve alerts.
• Fraud and AML solutions in one platform.

Fingerprint

Fingerprint is a device intelligence platform that helps businesses prevent fraud by identifying unique devices and their behaviour. It now identifies more than 1 billion unique devices every month. In 2026 it launched detection of authorised AI agents and AI assistants, helping businesses separate trusted automation from malicious bots.

Key features of Fingerprint:

• Device identification: a stable visitor ID based on browser and device attributes, even across incognito sessions.
• Smart Signals that detect bots, VPNs, tampering and other risk indicators.
• AI agent detection for the growing share of automated web traffic.
• Simple integration with existing platforms and workflows.

ThreatFabric

Amsterdam-based ThreatFabric specialises in mobile fraud prevention, combining mobile threat intelligence with behavioural analytics to stop malware-driven fraud, account takeover and social engineering scams, including APP and impersonation fraud. It protects more than 60 million online banking customers, and in October 2025 OneSpan made a strategic investment in the company.

Key features of ThreatFabric:

• Mobile threat intelligence on malware, malicious apps and fraud tactics targeting banking and fintech apps.
• Real-time detection of account takeover and unauthorised transactions.
• Scam detection for APP, impersonation and investment scams.
• Behavioural analytics, patented in Europe.

Feedzai

Feedzai is an AI-driven financial crime prevention platform trusted by financial institutions, retailers and payment processors. In 2025 it acquired Demyst for external data orchestration, raised $75 million at a valuation of over $2 billion, and was selected by the European Central Bank as the first-ranked provider of fraud detection for the digital euro.

Key features of Feedzai:

• AI and machine learning that analyse transactions in real time and reduce false positives.
• End-to-end risk management, from transaction monitoring and identity verification to behavioural analysis and reporting.
• Fraud and AML capabilities on one RiskOps platform.
• Customisable rules for industry-specific risk profiles.

Quantexa

London-based Quantexa uses data analytics and AI to uncover hidden fraud patterns that traditional systems might miss. It now positions its platform as Decision Intelligence. Quantexa raised $175 million at a $2.6 billion valuation in 2025, reports being named a Leader in the 2026 Gartner Magic Quadrant for Decision Intelligence Platforms, and in 2026 won a £175 million, 10-year partnership with HM Revenue & Customs.

Key features of Quantexa:

• Entity resolution and network analytics that map relationships between people, companies and transactions.
• Contextual AI that considers the broader context of each transaction.
• Real-time, data-driven decisions to stop fraud before losses occur.
• Scales with growing data volumes and new fraud risks.

SAS

SAS (SAS Institute) is a long-established analytics company headquartered in Cary, North Carolina. SAS Fraud Management monitors payments and non-monetary events in real time across channels, combining machine learning with rules, and is used by large banks such as HSBC.

Key features of SAS Fraud Management:

• Real-time scoring across cards, payments and digital channels.
• Machine learning models combined with configurable rules.
• Integrated alert and case management for investigators.
• Fraud and AML solutions available on the same analytics platform.

Signifyd

Signifyd is a fraud prevention platform for e-commerce businesses, offering AI-powered decisions on transactions in real time. Its Commerce Protection Platform now covers the full customer journey: account creation and login, checkout, and post-purchase abuse such as returns fraud. Signifyd refreshed its brand in September 2026 and serves retailers including Walmart and Albertsons.

Key features of Signifyd:

• Guaranteed fraud protection: approved orders are backed by a financial guarantee against chargebacks.
• Real-time approve or decline decisions based on transactional and behavioural data.
• Pre-authorisation screening that stops fraud before transactions reach the card issuer.
• Customisable policies by product, customer segment and transaction type.

Frequently asked questions about fraud detection software

What is fraud detection software?

Fraud detection software analyses transactions, devices, identities and user behaviour to spot and stop fraudulent activity, ideally in real time. It typically combines rules, machine learning and data such as device and network signals to score risk, block suspicious activity and route alerts to analysts for review.

How do I choose the right fraud detection software?

Start with the fraud types that matter most to your business, such as card fraud, account takeover or APP scams. Then compare solutions on real-time detection, accuracy (false positives and missed fraud), integration effort, scalability, explainability of AI decisions and total cost. Ask vendors for results from a pilot on your own data rather than relying on headline figures.

What is the difference between fraud detection and AML software?

Fraud detection focuses on stopping losses from unauthorised or deceptive activity, often within milliseconds. AML software focuses on identifying and reporting money laundering and terrorist financing, as required by AML/CFT regulation. The two overlap heavily, because fraud proceeds are often laundered, which is why many institutions now combine them in a FRAML (fraud and AML) approach.

Can AML software detect fraud?

Yes, to a large extent. AML transaction monitoring platforms such as AMLYZE use rules and scenarios that also catch fraud patterns, such as unusual spending, use of multiple cards or accounts, refund abuse and repeated rejected payments. Purpose-built fraud tools add capabilities like device fingerprinting and behavioural biometrics, so the two are often used together.

How is AI used in fraud detection?

AI models learn patterns from historical and real-time data to score risk more accurately than static rules alone, reducing false positives and catching new fraud patterns. Newer tools also use AI agents to help analysts investigate alerts. When evaluating AI features, check that decisions are explainable, that humans keep control over final decisions, and that models are monitored over time.

Which EU rules affect fraud prevention for payment providers?

In the EU, PSD2 already requires strong customer authentication and fraud reporting from payment service providers, and the Instant Payments Regulation introduced Verification of Payee for euro transfers. PSD3 and the Payment Services Regulation (PSR), expected to apply from 2027, will add more focus on fraud data sharing and impersonation scams. The exact obligations depend on your licence and services, so confirm them with your compliance or legal team.

You can also read:
Best AML Software Providers
Best Core Banking Software
Best KYC Solutions