12 Best Fraud Detection Software in 2026

Fraud now reaches a bank through several doors at once, including payments, new accounts, logins, and the documents customers submit. Rules written for last year’s pattern miss new schemes, while strict thresholds block good customers and bury analysts in alerts. 

Fraud detection software scores transactions, sessions, and applications as they happen, using behavior, device, identity, and document signals. It then flags the cases that need a human decision. 

Teams adopt it because it: 

• Stops losses before money leaves the account 
• Cuts false positives, so genuine customers are not declined 
• Explains each alert for auditors and regulators 
• Brings fraud and AML signals into one view 
• Handles rising volume without adding headcount 

For this guide, we researched over 30 different fraud detection platforms, went through their product documentation, and read customer reviews to see what users report about false declines and alert volume.  

We then narrowed our list down to the best 12. Each of the platforms is compared on their standout capability, features, and fit, so you can shortlist with confidence. 

Top Fraud Detection Software at a Glance 

A Deep Analysis of the Best Fraud Detection Software   

Fraud detection tools differ widely in how well they catch threats in real time, analyze behavior, and support compliance. This section breaks down the leading platforms, covering their strengths, limitations, and ideal use cases. Use it to find the solution that fits your risk profile, budget, and existing systems. 

1. Collatio by Scry AI 

Collatio by Scry AI is an intelligent document processing platform that checks purchase orders and invoices for fraud before they reach payment. It captures purchase orders from emails, supplier portals, shared folders, and repositories in more than 150 document formats, then applies three-way matching with configurable, rule-based controls across purchase orders, invoices, and receipts.  

Its accounts payable workflow extends the same checks to invoices, with 6-way matching and detection of duplicate, irregular, or fraudulent invoices. 

Two capabilities set it apart for finance teams. Anomaly detection compares each purchase order against historical trends, contract terms, and market benchmarks to surface abnormal pricing, unexpected suppliers, and large quantity deviations. Forensic checks, including signature verification and metadata analysis, look for fake or tampered documents that would pass a basic data review. 

On measured results, Scry AI’s own estimate puts accuracy at 92% for detecting fraudulent and counterfeit documents when OCR, NLP, forensic analysis, and anomaly detection work together, against 80% for legacy OCR systems. The same page is direct about the limit: AI cannot fully guarantee detection of advanced or novel schemes, so flagged documents still go to a reviewer. 

Pros and Cons 

Key features 

• Multi-format ingestion: Captures purchase orders from emails, supplier portals, shared folders, and repositories in more than 150 formats and multiple languages 
• Three-way matching: Validates purchase orders, invoices, and receipts against configurable rules 
• Anomaly detection: Flags abnormal pricing, unexpected suppliers, and large quantity deviations against historical trends 
• Duplicate and fraud checks: Surfaces duplicate, irregular, or fraudulent invoices before payment 
• Forensic document analysis: Uses signature verification and metadata checks to catch fake or tampered files 
• Audit-ready traceability: Logs full processing trails for compliance reporting 

2. Feedzai 

Feedzai is a financial crime platform that puts fraud prevention and anti-money laundering monitoring on a single platform for banks, payment providers, and acquirers. It consolidates behavioral, fraud, and AML monitoring across channels, so one team can see a customer’s activity in one place instead of switching between systems.  

Feedzai says its platform is used for account opening, payment authorization, transaction scoring, and merchant onboarding, and scam and authorized push payment fraud prevention sits on the same platform. 

Pros and Cons 

Key features 

• Unified monitoring: Brings behavioral, fraud, and AML monitoring together across channels 
• Account opening checks: Screens new accounts for fraud as they are created 
• Payment authorization and transaction scoring: Scores payments in real time as they are authorized 
• Acquirer risk management: Manages fraud risk for merchant acquirers and payment service providers 
• Watchlist screening: Screens customers against watchlists for compliance purposes 

3. Featurespace 

Featurespace builds the ARIC Risk Hub, a real-time machine learning platform that learns how each customer normally behaves and flags activity that departs from it. The company was founded in Cambridge in 2008 and has been part of Visa since the acquisition closed in December 2024.  

Its published case studies give concrete numbers. First Tech Federal Credit Union reported 35% fewer alerts and 84% less money lost to fraud between 2022 and 2024, and Eika completed its payment fraud implementation in five weeks. 

Pros and Cons 

Key features 

• Adaptive behavioral analytics: Models each customer’s normal behavior instead of relying only on aggregate patterns 
• Adaptive rules: Lets teams add new rules inside the platform, including cross-channel detection strategies 
• Real-time payment scoring: Assesses every online and mobile payment and can tell the bank to stop suspicious ones 
• Fraud and AML in one workflow: Combines fraud detection with AML case management 
• Flexible deployment: Runs as SaaS, which is PCI-DSS compliant and SOC 2 certified, or on-premise 

4. SAS Fraud Decisioning 

SAS Fraud Decisioning is a cloud-native platform on SAS Viya that scores 100% of transactions in real time with millisecond response times.  

It combines machine learning, predictive analytics, data orchestration, and case management, and ships with prebuilt fraud models for payment fraud, scams, money mules, funnel accounts, and synthetic identity fraud in new-to-bank relationships. SAS was named a Leader in The Forrester Wave: Enterprise Fraud Management, Q2 2024. 

Pros and Cons 

Key features 

• Real-time decisioning: Profiles, scores, and decisions transactions in milliseconds 
• Prebuilt fraud models: Extend existing rules and strategies to cover scams, mule accounts, and social engineering 
• Strategy testing: Compares models, rules, and data providers through champion-challenger and A/B tests 
• Data orchestration: Enriches fraud decisions with internal, external, and third-party data 
• Alert triage and case management: Includes dashboards, reporting, and investigation workflows 

5. NICE Actimize 

NICE Actimize’s IFM-X is a fraud hub that brings data management, analytic engines, and strategy and investigation management into one platform. Banks can run NICE Actimize’s own models, managed as a cloud service, or build their own on X-Sight Studio.  

In June 2026, DNB, Norway’s largest financial services group, selected the X-Sight Enterprise platform to consolidate screening, customer due diligence, and transaction and fraud monitoring on one SaaS platform with unified case management. 

Pros and Cons 

Key features 

• IFM-X fraud hub: Brings data management, analytics, and investigation management into one place 
• Collective intelligence: Applies industry-wide insights to fraud analytics 
• Model flexibility: Use NICE Actimize models or build your own in X-Sight Studio 
• ActOne case management: Uses analytics and AI to support fraud operations 
• Broad coverage: Spans new account fraud, payment fraud, employee fraud, and authentication management 

6. Hawk 

Hawk’s fraud prevention platform targets payment and card fraud, account takeover, scams, mule activity, and check fraud for banks, credit unions, payment companies, and fintechs. Hawk reports an average response time of 150 milliseconds and offers Day One Defense Models, prebuilt typology models that it says can be deployed in as little as three days.  

Hawk also pairs fraud with AML and screening, and its AML AI Overlay lets banks add AI to existing systems without replacing them. 

Pros and Cons 

Key features 

• Real-time prevention: Scores and decisions transactions in about 150 milliseconds on average 
• Day One Defense Models: Cover account takeover, merchant fraud, money mules, chargeback fraud, and APP scams 
• Scam and mule detection: Flags suspicious payment references and maps inflows and outflows to expose mule networks 
• Check fraud detection: Catches altered, forged, and counterfeit checks for US banks and credit unions 
• Unified case management: Adds contextual AI explanations that shorten review time 

7. Sardine 

Sardine combines device intelligence and behavior biometrics in a single SDK, so every login, onboarding step, and payment is scored with signals about both the device and the person using it.  

Behavior signals include typing and mouse movement, scrolling and swiping, copy and paste, and hesitation events, while device signals cover emulators, bots, and fingerprints.  

Sardine also adds customer due diligence, transaction monitoring, and case management on the same platform, and it was named a Leader in The Forrester Wave: Financial Crime Management Solutions, Q3 2026. 

Pros and Cons 

Key features 

• Behavior biometrics: Analyzes typing speed, mouse movement, scrolling and swiping patterns, and hesitation events 
• Device intelligence: Detects risky devices, emulators, and malicious bots 
• True Piercing: Unmasks fraudsters hiding their actual location or IP address 
• Identity fraud checks: Pairs its own signals with third-party data to produce one risk score 
• Fraud and compliance in one place: Adds transaction monitoring and case management for investigations 

8. SEON 

SEON enriches an email address, phone number, or IP address with a digital footprint that checks more than 350 social and digital platforms, which helps teams spot thin or fake online histories.  

It pairs that with device fingerprinting, machine learning, and AML screening, and its modular APIs suit fintechs, payment providers, iGaming operators, and e-commerce businesses that want to integrate quickly. 

Pros and Cons 

Key features 

• Digital footprint analysis: Checks email, phone, and IP against social and digital platforms to expose fake profiles 
• Device fingerprinting: Collects device data points to catch multi-accounting and fake users 
• Customizable rules and scoring: Lets teams set scoring rules around their own risk profile 
• Network scoring and link analysis: Connects accounts to uncover synthetic IDs and fraud rings 
• AML screening: Adds compliance checks to the same platform 

9. Sift 

Sift’s Digital Trust & Safety Platform covers payment protection, account defense, content integrity, and dispute management in one suite, which suits marketplaces and digital businesses that face fraud beyond the checkout page.  

Sift says it serves 700+ global brands and draws on signals from across its customer network, so a pattern seen at one business can inform a decision at another. Partner integrations through Sift Connect help it fit into existing stacks. 

Pros and Cons 

Key features 

• Payment protection: Scores transactions to stop payment fraud and reduce false declines 
• Account defense: Targets account takeover and fake account creation 
• Content integrity: Detects spam and abuse in user-generated content 
• Dispute management: Handles chargebacks from one console 
• Sift Connect: Adds partner integrations for existing systems 

10. Unit21 

Unit21 started as a no-code rules engine and relaunched in March 2026 as AI Risk Infrastructure, with agents that tune rules, investigate alerts, run workflows, and file suspicious activity reports end to end. It serves fintechs, banks, and credit unions, and says it works with more than 200 customers across 90 countries.  

Lili used the platform to cut fraud losses by 50% and investigation time by 75%, uncovering more than $400,000 in suspected fraud. 

Pros and Cons 

Key features 

• No-code rules engine: Lets fraud and compliance teams build detection scenarios without engineering support 
• AI agents: Investigate alerts, draft narratives, and file reports 
• Real-time monitoring and device intelligence: Adds device risk signals to fraud strategies 
• Consortium signals: Adds early-warning data from across the customer network 
• Unified case management: Keeps fraud and AML investigations in one workflow 

11. Trustmi 

Trustmi protects B2B payments rather than cards or consumer accounts. Its behavioral AI platform checks payments across email and ERP data to detect business email compromise, vendor impersonation, invoice manipulation, and deepfake scams before money moves.  

In August 2026 it launched an AI Investigation Agent that connects signals from communications, documents, vendors, and financial systems to give security and finance teams evidence-backed outcomes.  

Pros and Cons 

Key features 

• Behavioral AI: Builds baselines from communications, documents, vendors, and financial systems 
• Business email compromise and deepfake detection: Flags socially engineered requests before payment 
• Vendor onboarding and account validation: Checks vendor bank details before they enter the payment run 
• Payment verification: Gives a safe or unsafe verdict on each B2B payment before money moves 
• AI Investigation Agent: Supports collaborative, evidence-backed fraud investigations 

12. Fraud.net 

Fraud.net combines AI and deep learning, collective intelligence, rules-based decision engines, and streaming analytics in one architecture for enterprises in financial services and digital commerce.  

It describes a cloud-born platform with a no-code and low-code design and natively integrated data from more than 50 third-party providers, covering fraud, AML, and KYC workflows. Its collective intelligence draws on billions of transactions across customers. 

Pros and Cons 

Key features 

• Collective intelligence: Learns from transaction patterns across its customer network 
• Transaction and application AI: Scores payments and screens onboarding and form submissions 
• Hybrid decision engine: Combines AI, deep learning, and configurable rules 
• Data integrations: Brings in data from more than 50 third-party providers 
• Synthetic identity detection: Flags fabricated identities during onboarding 

Also Read: Invoice Fraud Detection 

Our Methodology For Ranking the Best Fraud Detection Software 

We built this list by reading each vendor’s official website, product documentation, and published case studies, then cross-checking claims against peer review sites. We ranked the platforms against the factors below. 

1. Detection Approach 

How each platform combines machine learning, behavioral analytics, and rules, and how clearly it explains a decision.  

2. Real-time performance 

Response time at authorization or payment, and whether the platform can hold or block activity as it happens.  

3. Fraud coverage 

Payment, account takeover, new-account, scam, mule, and document fraud 

4. Integration and deployment 

APIs, SDKs, cloud and on-premise options, and how long implementation takes 

5. Security, audit, and review signals 

Certifications, audit trails, case management, and what verified users report on independent review sites 

How to Choose the Best Fraud Detection Software 

The right platform depends on where fraud reaches your business and how much operational capacity your team has. Work through three steps before you book any demo. 

Step 1: Map your fraud exposure and pain points 

List the fraud types that cost you the most, such as card-not-present fraud, account takeover, authorized push payment scams, duplicate invoices, or vendor impersonation. Then look at where your current process falls short. Check how many alerts your analysts clear each week, what share turn out to be legitimate, and how long a case takes from alert to decision. These numbers become your baseline for judging any vendor. 

Step 2: List must-have capabilities 

Separate the capabilities you cannot do without from the nice-to-haves. For payment fraud, that usually means response time at authorization, the ability to hold or block, and explainable decisions that satisfy auditors. For onboarding fraud, device and behavioral signals matter more.  

For finance teams, document checks such as three-way matching and duplicate detection come first. Add integration needs, deployment preferences, and the certifications your security team requires. 

Step 3: Compare and validate vendors 

Ask for a demo and then a proof of concept on your own historical data, including confirmed fraud cases and legitimate activity that looks unusual. Measure fraud caught, false positives, and analyst time per case.  

Confirm how the platform connects to your core banking, ERP, or payment systems, ask for security documentation, and check what support looks like after go-live, including time zone coverage. Request pricing against the same volume assumptions from every vendor so quotes can be compared. 

Bonus Read: Accounts Payable Fraud