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Fraud prevention has become a core part of digital business operations. Fintech companies, online marketplaces, e-commerce brands, iGaming operators, payment businesses, and other digital platforms need to evaluate risk without creating unnecessary friction for legitimate customers.
That makes choosing a fraud prevention platform more complicated than simply comparing feature lists. Organizations need to consider API architecture, device and identity intelligence, risk scoring, custom rules, AML screening, investigation workflows, implementation requirements, and pricing.
This guide compares SEON vs Sift vs Sardine in 2026, with a closer look at where each platform fits and what businesses should consider before choosing one.
SEON vs Sift vs Sardine at a Glance
| Area | SEON | Sift | Sardine |
| Fraud prevention | Yes | Yes | Yes |
| API-based integration | Yes | Yes | Yes |
| Custom decisioning | Custom rules and scoring | Custom/industry models and decisioning | Rules and custom model capabilities |
| Device intelligence | Yes | Risk intelligence and device-related signals | Device and behavioral signals |
| Phone/IP/BIN intelligence | Yes | Broader risk-data approach | Broad fraud intelligence |
| AML screening | Yes | Compliance and risk controls | AML and financial crime capabilities |
| Case management | Yes | Yes | Yes |
| Main focus | Fraud + digital risk + AML | Fraud, abuse and digital trust | Fraud, identity and financial crime |
| Published self-service pricing | $699/month Starter | Contact vendor | Contact vendor |
The exact capabilities and packaging can change, so organizations should confirm current requirements and pricing directly with each provider before making a procurement decision.
1. SEON: Fraud Prevention With an API-First Approach
For businesses looking for a combination of fraud detection, digital intelligence, decisioning, and AML capabilities, SEON takes an API-first approach.
Its Fraud API combines email, phone, IP, BIN and AML data with device fingerprinting, allowing organizations to request enriched information, rules and scoring through a unified API. The architecture is modular, so businesses can enable or disable different components according to their requirements.
Businesses can explore the platform through SEON’s official website.
Key SEON Features
Fraud API and Decisioning
SEON’s Fraud API is designed to bring multiple fraud signals into the decisioning process. It can incorporate:
- Phone intelligence
- IP intelligence
- BIN information
- Device intelligence
- AML information
- Custom fields
- Rules and scoring
The API can therefore be used as a central component of a broader risk-management workflow rather than requiring every signal to be evaluated through a separate integration.
Phone, IP and BIN Intelligence
SEON provides dedicated APIs for several data types.
Its Phone API can provide information associated with a phone number, while the IP API is designed to identify suspicious VPN and proxy connections and provide geolocation-related information. Its BIN API provides information such as card issuer, country, card type and validity.
This can be useful when a company wants to combine identity and payment-related signals with transaction or account information.
Device Intelligence
Device Intelligence adds another layer to fraud assessment by analyzing device and behavioral information. SEON documents integrations for web, iOS and Android environments, with device fingerprinting and behavioral signals connected to its Fraud API.
This can be relevant for detecting suspicious activity around registration, login, checkout and other sensitive parts of the customer journey.
Custom Rules, Scoring and Decisioning
Not every company has the same fraud patterns. A payment processor may care about different signals than an e-commerce marketplace or an iGaming operator.
SEON allows teams to build custom rules and use its scoring engine for decisioning. Custom data can also be passed through the API and used in rules.
This level of customization can be particularly useful for organizations that want more control over how individual risk signals affect their workflows.
SEON’s AML and Compliance Capabilities
Fraud prevention and AML screening often overlap, particularly for fintech and financial services businesses.
SEON provides AML screening that can check customers against sources including:
- Sanctions lists
- PEP databases
- Watchlists
- Crime lists
- Adverse media
The platform also supports configurable fuzzy matching and ongoing monitoring.
Its AML documentation also emphasizes that potential matches should be reviewed manually rather than treated as an automatically conclusive compliance decision.
That distinction is important: screening software provides information and risk signals, but an organization’s compliance team remains responsible for interpreting results within its applicable policies and regulations.
Case Management
SEON also includes case-management capabilities for investigating fraud and AML alerts. Its documentation describes workflows for reviewing screening results, investigating cases, recording analyst activity and managing alerts.
For organizations handling a growing number of alerts, having investigation functionality connected to the same risk environment can reduce the need to move information between unrelated systems.
SEON Pricing in 2026
SEON currently publishes two main plans:
Starter — $699/month
The Starter plan includes:
- 2,500 fraud checks per month
- 10 users
- 50 custom rules
- Platform and API access
- Implementation assistance through support tickets and email
- Basic monitoring
- Standard reporting
Premium — Custom Pricing
The Premium plan includes:
- Unlimited API calls
- Unlimited users
- Unlimited custom rules
- Case Management
- AML Compliance
- Dedicated implementation support
- 24/7 support
- Advanced monitoring and reporting
- Managed Risk Services
These details are based on SEON’s published pricing information.
Potential SEON Limitations
SEON’s Starter price may be relatively significant for very small organizations with limited fraud-check volumes. Businesses that need Premium capabilities must request custom pricing.
There can also be technical and operational requirements around API integration, rule configuration, monitoring and investigation workflows. Organizations should evaluate whether they have the engineering and risk-management resources needed to use these capabilities effectively.
Most importantly, fraud and AML screening results should be evaluated within the company’s own risk, compliance and investigation processes rather than treated as automatic guarantees of fraud prevention or regulatory compliance.
2. Sift: Broad Fraud and Digital Trust Coverage
Sift takes a broader digital-trust approach to fraud prevention, covering activities such as account creation, login, transactions and post-transaction activity.
Its platform supports payment fraud, account takeover, fake accounts, e-commerce fraud and marketplace-related abuse. Sift describes its system as combining data ingestion, risk evaluation, decisioning, automated actions, reporting, case management and workflow analysis.
Sift API and Integration
Sift provides REST APIs, JavaScript integrations and mobile SDKs. Its developer documentation also describes support for sending events, fighting different types of fraud and automating fraud decisions.
This makes Sift suitable for organizations that want to integrate fraud decisions into existing customer journeys rather than relying only on a standalone dashboard.
Sift Decisioning
Sift’s platform uses customer and event data alongside risk models and configurable decisioning. Its platform documentation highlights control over risk signals, models and workflows.
Its approach may appeal to businesses dealing with multiple forms of digital abuse rather than payment fraud alone.
Potential Sift Use Cases
Sift documents use cases including:
- Account takeover prevention
- Payment fraud
- Fake-account prevention
- E-commerce protection
- Marketplace fraud
- Post-transaction abuse
The platform is therefore relevant to businesses where fraud can occur at multiple points in the customer lifecycle.
Pricing is not presented in the same publicly itemized format as SEON’s $699 Starter plan, so companies evaluating Sift should request current commercial terms based on their use case and volume.
3. Sardine: Fraud, Identity and Financial Crime
Sardine approaches fraud prevention through a combination of identity, device, behavioral and financial-crime risk capabilities.
Its fraud technology includes device and behavioral signals, network intelligence, AI-based capabilities, case management and model deployment options. Sardine also documents customizable API-based data ingestion and support for deploying proprietary or customer-developed models.
Device and Behavioral Intelligence
Device and behavioral information can provide additional context around a user’s activity.
This is particularly relevant when organizations need to identify suspicious behavior that may not be obvious from traditional transaction information alone.
Custom Models and Rules
Sardine’s platform documentation describes a Model Garden for deploying Sardine’s models as well as customers’ own models. These models can work with its rules engine and score normalization.
That approach may be interesting for organizations with mature risk teams that want to incorporate their own models into a broader fraud decisioning environment.
Case Management
Sardine also documents case-management functionality for investigating alerts, collaborating with teams and resolving fraud cases.
For businesses with dedicated fraud operations, investigation workflows can be as important as the initial risk score.
SEON vs Sift vs Sardine: Feature Comparison
The three platforms overlap, but their approaches are not identical.
| Capability | SEON | Sift | Sardine |
| Fraud API | Strong modular API approach | API and SDK integrations | API-based architecture |
| Phone intelligence | Yes | Risk intelligence approach | Available within broader risk capabilities |
| IP intelligence | Yes | Yes, through broader risk signals | Device/network intelligence |
| BIN intelligence | Yes | Payment-risk capabilities | Payment and financial risk focus |
| Device intelligence | Yes | Yes | Yes |
| Custom rules | Yes | Yes | Yes |
| Risk scoring | Yes | Yes | Yes |
| AML screening | Strong focus | Risk/compliance capabilities | Financial crime focus |
| PEP/sanctions/watchlists | Yes | Compliance-related capabilities | Financial crime capabilities |
| Adverse media | Yes | Not positioned as the core differentiator | Financial crime capabilities |
| Case management | Yes | Yes | Yes |
| Custom model deployment | Not the primary positioning | Models and decisioning | Explicit model deployment capability |
The table is intended as a high-level comparison rather than a complete product specification. Individual capabilities, modules and availability can depend on plan, implementation and use case.
Which Businesses Can Use These Platforms?
Fintech and Financial Services
Fintech companies often need to evaluate customers during onboarding, account access, payments and money movement.
SEON’s combination of fraud signals, device intelligence and AML screening can be relevant where fraud and compliance workflows need to operate together. Sift and Sardine also address financial and digital-risk scenarios, although their product structures and emphasis differ.
E-Commerce
E-commerce businesses can face payment fraud, account takeover, fake accounts and other forms of abuse.
Sift explicitly positions its platform around e-commerce and payment protection, while SEON provides transaction, device, IP, phone and BIN-related intelligence. Sardine’s device and behavioral capabilities can also be relevant to digital commerce risk management.
Marketplaces
Marketplaces have to think about both sides of the transaction.
Fraud risks can involve buyers, sellers, accounts, payments and unusual behavioral patterns. Sift specifically lists commerce marketplaces among its use cases, while SEON and Sardine provide broader risk and fraud capabilities that can be applied to marketplace workflows.
iGaming
iGaming businesses can have complex requirements around account creation, payments, device activity, location and responsible-gaming or compliance workflows.
SEON’s documentation includes iGaming-related use cases and dedicated capabilities such as device intelligence and self-exclusion API functionality.
SEON vs Sift vs Sardine: What Should You Evaluate?
Instead of choosing purely from a feature checklist, businesses should examine how each platform fits their existing fraud operation.
1. Integration Requirements
Ask how much engineering work is required to send the right events and receive decisions at each stage of the customer journey.
An API-first platform can be useful when fraud decisions need to become part of existing application workflows.
2. Signal Coverage
Consider which signals actually matter for your fraud patterns.
For example, a business might prioritize:
- Device intelligence
- IP reputation
- Phone intelligence
- BIN data
- Behavioral signals
- Transaction history
- AML data
- Custom customer data
More signals do not automatically mean better decisions. The important question is whether the available information is relevant to your risk model.
3. Rules and Decisioning
Fraud patterns change over time.
Look at how easily risk teams can create, test, modify and monitor rules. Also consider how the platform handles scoring, automated actions and manual review.
4. AML Requirements
For regulated businesses, fraud prevention may only be one part of the requirement.
Check whether the platform supports the specific screening sources and workflows your compliance team needs, including sanctions, PEPs, watchlists, crime lists and adverse media.
5. Investigation Workflows
A fraud alert is only the beginning of an investigation.
Case management, analyst notes, alert history, evidence and audit trails can become increasingly important as transaction volume grows.
6. Pricing and Total Implementation Effort
Don’t compare subscription prices alone.
Consider:
- API usage
- Number of users
- Fraud-check volume
- Custom rules
- AML screening requirements
- Support requirements
- Implementation work
- Internal engineering resources
- Fraud analyst workload
A platform with more functionality may also require more operational effort to configure and manage effectively.
SEON vs Sift vs Sardine: Pros and Limitations
SEON
Potential strengths
- API-first architecture
- Modular Fraud API
- Phone, IP and BIN intelligence
- Device intelligence
- Custom rules and scoring
- Fraud and AML capabilities in one ecosystem
- Case management
- Published Starter pricing
Potential limitations
- Starter pricing may not suit every small business
- Premium pricing requires a custom quote
- API integration requires technical resources
- Advanced fraud and AML workflows may require dedicated operational oversight
Sift
Potential strengths
- Broad digital fraud and abuse coverage
- Payment fraud and account protection
- Marketplace and e-commerce use cases
- API, SDK and integration options
- Decisioning and workflow capabilities
- Case management
Potential limitations
- Commercial pricing generally requires direct evaluation
- Its broad platform may require careful configuration around a company’s specific risk workflows
- Organizations should assess which modules and capabilities are necessary rather than assuming every feature is needed
Sardine
Potential strengths
- Device and behavioral intelligence
- Financial crime and fraud focus
- API-based architecture
- Case management
- Custom model deployment
- Rules and decisioning capabilities
Potential limitations
- Businesses may need technical expertise to take advantage of advanced model and API capabilities
- Pricing should be confirmed directly with the provider
- The usefulness of custom models depends on the organization’s data, risk strategy and operational maturity
Which Platform Fits Your Fraud Strategy?
There isn’t one universal fraud prevention architecture for every business.
SEON is worth evaluating when a company wants an API-first fraud platform that brings together digital intelligence, device signals, customizable decisioning and AML capabilities. Its modular Fraud API can combine phone, IP, BIN, AML and device information, while its broader platform includes case management and investigation functionality.
Sift may be relevant to organizations focused on broad digital trust, payment fraud, account takeover, fake accounts, e-commerce and marketplace abuse. Its platform covers multiple stages of the customer journey and provides APIs, decisioning and workflow capabilities.
Sardine can be considered by organizations looking for fraud and financial-crime capabilities that include device and behavioral intelligence, case management and customizable model infrastructure.
The most useful comparison ultimately depends on your fraud types, transaction volume, regulatory obligations, technical architecture and internal risk team.
Final Thoughts
The SEON vs Sift vs Sardine comparison is less about finding a universally superior platform and more about matching the technology to the way your organization manages risk.
SEON stands out for its combination of a modular Fraud API, digital intelligence, device intelligence, customizable rules, AML screening and case-management capabilities. Sift provides broad coverage across digital fraud and abuse, while Sardine combines fraud, device, behavioral and financial-crime capabilities with options for custom model deployment.
Before purchasing, run each platform against real business scenarios such as account creation, login, payment, withdrawal, suspicious device activity and AML screening. That practical evaluation can reveal which architecture and workflow best fit your organization’s actual fraud-management process.
