Fraud.net
AI-powered platform for real-time fraud detection and risk analytics at scale
- Category
- Data Analysis
- Pricing
- Custom enterprise pricing based on transaction volume and modules used; no public self-serve pricing tiers, sold via sales-led contracts.
- Best for
- Fraud.net is built for payments processors, e-commerce businesses, banks, and fintech companies that process meaningful transaction volume and need real-time, ML-driven fraud detection combined with identity verification and compliance tooling.
- Official site
- www.fraud.net
- Last updated
- August 2026
Fraud.net provides an AI-driven fraud prevention and risk analytics platform aimed at businesses that process significant volumes of transactions and need to detect fraudulent activity in real time. Rather than relying solely on static rules, the platform layers machine learning models on top of a configurable rules engine, allowing risk teams to adapt to new fraud patterns as they emerge while still retaining deterministic controls where needed.
A notable part of Fraud.net's approach is its collective intelligence network, which aggregates anonymized fraud signals across its customer base. This allows the models to learn from fraud patterns observed across many merchants and industries, theoretically improving detection of new or fast-moving fraud schemes faster than any single company's data alone could support.
Beyond transaction fraud, the platform extends into identity verification, KYC/AML compliance, and account takeover protection, positioning itself as a broader risk management suite rather than a single-purpose fraud scoring tool. It's typically adopted by payments processors, e-commerce platforms, banks, and fintechs that need to combine multiple risk signals into a unified decisioning workflow, and is sold through custom enterprise contracts rather than published pricing tiers.
Fraud.net is built for payments processors, e-commerce businesses, banks, and fintech companies that process meaningful transaction volume and need real-time, ML-driven fraud detection combined with identity verification and compliance tooling. It suits risk and fraud teams that want both adaptive machine learning and rules-based control in one platform. Very small merchants or businesses with low transaction volumes and simple fraud-risk profiles may find lighter-weight, self-serve fraud tools more appropriate.
Key features
Real-time ML transaction scoring
Scores transactions as they happen using machine learning models trained to detect fraudulent patterns with low latency.
Collective intelligence network
Aggregates anonymized fraud signals across Fraud.net's customer base to help detect emerging fraud schemes faster.
Configurable rules engine
Lets risk teams layer deterministic business rules alongside adaptive ML scoring for more controllable decisioning.
Identity verification and KYC/AML
Provides tools for verifying customer identity and supporting anti-money-laundering and compliance requirements.
Case management workflows
Gives fraud analysts tools to investigate flagged transactions, track case outcomes, and refine detection rules over time.
Payments and platform integrations
Offers APIs to integrate fraud scoring into payment processors, e-commerce platforms, and banking systems.
Pricing breakdown
Standard
- Core ML fraud scoring
- Rules engine access
- Standard API integrations
- Basic case management
Enterprise
- Higher transaction volume support
- Full collective intelligence network access
- Identity verification and KYC/AML modules
- Dedicated support and custom model tuning
Pros and cons
Pros
- Layering ML models on top of a rules engine gives risk teams both adaptive detection and deterministic control over decisioning
- The collective intelligence network can help surface fraud patterns learned from other merchants, potentially catching schemes faster than isolated internal data would
- Covering payments fraud, identity verification, and AML/KYC in one platform reduces the need to stitch together multiple point solutions
- Real-time scoring capability supports use cases where transaction decisions must be made in milliseconds
- Case management tools help fraud analyst teams operationalize investigations rather than just receiving raw risk scores
Cons
- Lack of transparent, published pricing makes it harder for smaller businesses to quickly assess affordability
- Integrating a fraud platform into existing payment/transaction flows typically requires meaningful engineering and data-integration effort
- Model performance and false-positive rates depend on quality of historical fraud/transaction data provided, which may need tuning post-launch
- As an enterprise-oriented platform, it may be less cost-effective for very small merchants with low transaction volumes
- Reliance on a shared intelligence network means detection quality may vary depending on how much relevant cross-customer data exists for a given industry
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Compare →Frequently asked questions
What is Fraud.net used for?
Fraud.net is used to detect and prevent fraud in real time across payments, e-commerce transactions, banking, and identity verification workflows using AI/ML models.
How does Fraud.net's collective intelligence network work?
It aggregates anonymized fraud signals across Fraud.net's customer base so its models can learn from fraud patterns observed across many merchants and industries.
Does Fraud.net only use machine learning, or also rules?
It combines adaptive machine learning models with a configurable rules engine, letting risk teams set deterministic controls alongside ML-based scoring.
Does Fraud.net support identity verification and compliance?
Yes, the platform includes identity verification tools and features to support KYC/AML compliance requirements.
How is Fraud.net priced?
Pricing is custom and sales-led, based on transaction volume and which modules (e.g., identity verification, AML) a business uses; there are no public self-serve tiers.
Who typically uses Fraud.net?
Payments companies, e-commerce platforms, banks, and fintechs that need real-time fraud detection and risk scoring at meaningful transaction volume.
Ready to try Fraud.net?
Head to the official site to explore pricing and start a free trial where available.
Visit Fraud.net →