---
title: "Stripe Radar vs Sift vs Castle: Fraud Detection for Apps 2026"
author: "Nate Laquis"
author_role: "Founder & CEO"
date: "2030-02-02"
category: "Technology"
tags:
  - fraud detection
  - Stripe Radar
  - Sift
  - Castle
  - payment security
excerpt: "Online fraud costs businesses over $48 billion annually and is growing 25% year over year. Stripe Radar, Sift, and Castle each take a fundamentally different approach to stopping it. Here is how to pick the right fraud detection platform for your application."
reading_time: "14 min read"
canonical_url: "https://kanopylabs.com/blog/stripe-radar-vs-sift-vs-castle"
---

# Stripe Radar vs Sift vs Castle: Fraud Detection for Apps 2026

## Online Fraud Is a $48 Billion Problem, and It Is Getting Worse

Online fraud surpassed $48 billion in global losses in 2025, and it is growing at roughly 25% year over year. That number is not just credit card chargebacks. It includes account takeover, synthetic identity fraud, promo abuse, refund manipulation, and a growing list of attack vectors that did not exist five years ago. If you process payments, manage user accounts, or run any kind of marketplace, fraud is already costing you money. You just might not know how much yet.

The real damage goes beyond direct financial loss. Chargebacks trigger penalties from payment processors. False positives block legitimate customers and kill conversion rates. Account takeovers destroy user trust. And every hour your team spends manually reviewing flagged transactions is an hour not spent building your product.

Three platforms dominate fraud detection for software applications in 2026: Stripe Radar, Sift, and Castle. They overlap in some areas, but each one makes a fundamentally different architectural bet about how fraud should be detected and stopped. Stripe Radar is deeply integrated into the payment flow and uses Stripe's massive transaction network for ML signals. Sift operates as a cross-platform fraud decision engine covering payments, account security, and content abuse. Castle focuses on device fingerprinting and real-time account protection with an emphasis on stopping threats before they reach the transaction layer.

![Security monitoring dashboard displaying fraud detection metrics and compliance alerts](https://images.unsplash.com/photo-1563986768609-322da13575f2?w=800&q=80)

Choosing the wrong tool here is expensive. Migrate halfway through a growth phase and you lose months of trained ML models. Pick a tool that does not cover your primary fraud vector and you are bolting on workarounds from day one. This comparison breaks down the technical differences that actually matter so you can make the right call before writing a single line of integration code.

## Stripe Radar: ML-Powered Fraud Detection Native to Stripe

Stripe Radar is the fraud detection layer built directly into Stripe's payment infrastructure. If you already use Stripe for payment processing, Radar is running on every transaction by default. The free tier (Radar for Fraud Teams is $0.07 per screened transaction on top of standard Stripe fees) uses machine learning trained on billions of transactions across millions of Stripe accounts worldwide. That network effect is Radar's single biggest advantage: it sees fraud patterns across the entire Stripe ecosystem, not just your account.

### How the ML Model Works

Radar's machine learning model evaluates hundreds of signals per transaction. These include card fingerprint data, billing and shipping address mismatches, IP geolocation, velocity checks (how many transactions this card has attempted in the last hour), device characteristics, and behavioral signals from the checkout session. The model assigns a risk score between 0 and 99 to every charge. Transactions above your configured threshold get blocked automatically, and those in the middle range can be routed to manual review.

What makes Radar's ML genuinely strong is cross-merchant learning. When a stolen card gets flagged on one Stripe account, that signal immediately improves fraud detection across every other Stripe account. If you are a small startup processing 500 transactions per month, you still benefit from the fraud patterns Stripe learned from merchants processing millions of transactions daily. No standalone fraud tool can replicate this network effect without building a similarly large merchant network.

### The Rules Engine

Radar for Fraud Teams adds a customizable rules engine on top of the ML model. You can write rules in Stripe's domain-specific language to block, allow, or flag transactions based on metadata, card attributes, customer history, or risk score. Examples include blocking all transactions where the billing country does not match the IP country, requiring 3D Secure for charges above $500, or automatically allowing repeat purchases from customers with a clean history.

The rules engine supports both "block" and "review" actions. Blocked transactions are declined instantly. Review transactions are held for manual review in the Stripe Dashboard, where your team can approve or reject them with additional context. You can also use rules to request 3D Secure authentication selectively, pushing the liability for chargebacks to the card issuer without adding friction to every transaction.

### Strengths and Limitations

Radar's biggest strength is zero integration effort for existing Stripe users. It is already running. Turning on Radar for Fraud Teams is a toggle, not a migration project. The ML model is world-class because of the network effect, and the rules engine gives you enough customization for most use cases.

The limitation is scope. Radar only protects Stripe transactions. If you process payments through multiple gateways, Radar does not see the non-Stripe volume. It also does not cover account takeover, login fraud, content abuse, or any fraud vector that happens before or outside the payment flow. For teams [building a fintech app](/blog/how-to-build-a-fintech-app) on Stripe, Radar is the obvious starting point. For teams with fraud problems beyond payments, it is necessary but not sufficient.

## Sift: Cross-Platform Fraud Intelligence Across the Entire User Journey

Sift takes a fundamentally broader approach than Stripe Radar. Instead of focusing on a single payment processor, Sift positions itself as a cross-platform fraud decision engine that covers the entire user lifecycle: account creation, login, content posting, payment, and post-transaction activity like refund requests and chargebacks. Sift works with any payment processor, any authentication system, and any application architecture.

### The Digital Trust and Safety Platform

Sift organizes its product around "workflows" that map to specific fraud vectors. Payment Protection handles transaction fraud and chargeback prevention. Account Defense covers account takeover and credential stuffing. Content Integrity detects spam, scams, and policy-violating user-generated content. Dispute Management automates chargeback responses. Each workflow has its own ML model trained on data from Sift's global network of 34,000+ sites and apps.

You send events to Sift's API as users interact with your application: $create_account, $login, $create_order, $update_password, and dozens of other event types. Sift's ML models evaluate each event in real time and return a risk score between 0 and 100. You configure decision thresholds to automatically block, allow, or queue events for manual review. The console provides a unified view of each user's risk profile across all event types, so your fraud team can see that the same user who just attempted a suspicious login also submitted three refund requests last week.

### Machine Learning and Manual Rules

Sift's ML models are trained on labeled fraud data from its entire customer network, similar to Stripe Radar's network effect but spanning all transaction types, not just payments. The models adapt to new fraud patterns automatically, but you can also create manual rules using Sift's Workflows builder. Rules can reference any field in your event data, Sift-computed signals like device fingerprint reputation, and aggregated metrics like "number of failed payment attempts by this device in the last 24 hours."

One feature that sets Sift apart is its ability to ingest and score custom event types beyond the standard set. If your application has a unique interaction pattern (for example, a marketplace where sellers set prices and buyers submit offers), you can define custom events and Sift's models will incorporate them into the risk scoring. This flexibility matters for platforms with non-standard user flows that do not map cleanly to "buyer places order" templates.

### Strengths and Limitations

Sift's biggest advantage is coverage breadth. A single platform covers payment fraud, account security, and content abuse with shared intelligence across all three. If a fraudulent actor creates a fake account, Sift scores that event. When the same actor attempts a payment two days later, the account-creation signal feeds into the payment risk score. This cross-workflow intelligence is something you cannot replicate by stitching together separate tools for each fraud type.

![Secure online payment checkout flow with fraud verification indicators](https://images.unsplash.com/photo-1556742049-0cfed4f6a45d?w=800&q=80)

The limitations are cost and complexity. Sift is enterprise-priced, typically starting around $25,000 to $30,000 per year for smaller volumes, with per-event pricing that scales with your traffic. Integration is also heavier than Radar. You need to instrument your application to send events at every meaningful touchpoint, which can take weeks or months depending on the size of your codebase. For early-stage startups processing under $1M in annual transactions, Sift is often overkill both in capability and cost.

## Castle: Device Intelligence and Account Security First

Castle approaches fraud from the device and identity layer rather than the transaction layer. Its core thesis is that most fraud starts with compromised accounts, stolen sessions, or bot-driven automation, and the best place to stop it is before the fraudster ever reaches a payment form. Castle's primary products are device fingerprinting, real-time risk scoring, and adaptive authentication policies.

### Device Fingerprinting and Risk Scoring

Castle's JavaScript and mobile SDKs collect device signals (browser configuration, screen resolution, installed fonts, WebGL renderer, timezone, language) and behavioral signals (typing patterns, mouse movements, touch pressure) to create a persistent device fingerprint. This fingerprint persists across sessions, incognito windows, and even some VPN configurations. When a user logs in or performs a sensitive action, Castle evaluates the device fingerprint against the user's historical device profile and returns a risk assessment.

The risk assessment includes a score, a recommended action (allow, challenge, deny), and detailed signals explaining why the score is what it is. Signals might include "new device for this user," "device previously associated with a blocked account," "IP address changed country since last session," or "bot-like interaction patterns detected." Your application uses these signals to decide whether to allow the action, require step-up authentication (MFA, email verification), or block it entirely.

### Policies and Adaptive Authentication

Castle's policy engine lets you define rules that trigger specific authentication requirements based on risk signals. For example: require MFA when a user logs in from a new device, block login attempts from devices flagged in a known botnet, or force re-authentication when a session's device fingerprint changes mid-session (a strong indicator of session hijacking). Policies are configured in Castle's dashboard and enforced via the API, so you do not need to redeploy your application to update your fraud rules.

The adaptive authentication approach is Castle's most differentiated feature. Instead of applying the same authentication requirements to every user (which creates unnecessary friction for legitimate users), Castle adjusts requirements based on real-time risk. A returning user on a recognized device gets frictionless access. The same user on a new device in a new country gets prompted for MFA. A device that matches known fraud patterns gets blocked before the password prompt even appears.

### Strengths and Limitations

Castle excels at account-level fraud. If your primary concern is account takeover, credential stuffing, session hijacking, or bot-driven account creation, Castle provides more depth and sophistication than either Radar or Sift in this specific area. The device fingerprinting is best-in-class, and the adaptive authentication policies reduce friction for good users while raising the bar for attackers.

The limitation is that Castle is not a payment fraud tool. It does not analyze transaction amounts, billing addresses, or card data. It does not handle chargebacks or dispute management. Castle protects the account. Something else (Stripe Radar, Sift, or your own logic) needs to protect the payment. Castle's pricing is more accessible than Sift's, typically starting around $500 per month for smaller applications, with volume-based pricing that scales with monthly active users rather than events or transactions.

## Head-to-Head Comparison: False Positives, Chargebacks, Device Intel, and Cost

The differences between these three tools crystallize when you compare them on the five metrics that matter most for production fraud prevention: false positive rates, chargeback reduction, device intelligence depth, rule customization, and cost per transaction screened.

### False Positive Rates

False positives are blocked legitimate transactions. They cost you revenue and frustrate your best customers. Stripe Radar claims false positive rates below 0.1% for most merchants, driven by the cross-merchant ML model that has seen enough legitimate transaction patterns to avoid over-blocking. Sift reports similar numbers but with more variance depending on how well you have tuned your decision thresholds and custom rules. Castle's false positive metric applies to authentication challenges rather than blocked payments. Its adaptive approach keeps unnecessary MFA prompts below 5% of logins for most implementations.

In practice, false positive rates depend heavily on your specific business. High-risk verticals like digital goods, international e-commerce, and crypto on-ramps will see higher false positives across all three tools. The key differentiator is tunability. Radar's rules engine, Sift's Workflows builder, and Castle's policy engine all let you adjust thresholds, but Sift offers the most granular control because it scores more event types with more configurable inputs.

### Chargeback Reduction

Stripe Radar focuses heavily on chargeback prevention and reports that merchants using Radar for Fraud Teams see 25% fewer chargebacks on average compared to the basic Radar ML alone. The 3D Secure integration is particularly effective here: by shifting liability to the card issuer on high-risk transactions, you eliminate a category of chargebacks entirely rather than just blocking suspicious charges.

Sift's chargeback reduction numbers are harder to generalize because they depend on which workflows you deploy and how mature your integration is. Sift publishes case studies showing 50% to 80% chargeback reductions for specific merchants, but these are typically large enterprises with mature integrations and dedicated fraud teams. Sift's Dispute Management workflow can also automate chargeback responses, which recovers revenue on disputes you would otherwise lose by default.

Castle does not directly reduce chargebacks because it does not operate at the payment layer. However, by preventing account takeover, Castle eliminates the upstream cause of many chargebacks. A significant portion of "friendly fraud" and unauthorized purchase chargebacks originate from compromised accounts, not stolen cards. Stopping the account takeover stops the fraudulent transaction before it happens.

### Device Intelligence

Castle leads this category by a wide margin. Device fingerprinting is Castle's core technology, and it collects more signals, tracks devices more persistently, and provides richer device reputation data than either Radar or Sift. Castle can detect when a single device is associated with multiple accounts (a strong fraud indicator on marketplaces), when a device's fingerprint has been spoofed, and when automation tools like Selenium or Puppeteer are driving interactions.

Stripe Radar collects device data through Stripe.js, but it is limited to what the Stripe checkout session exposes. It does not track devices across non-payment interactions. Sift collects device signals through its JavaScript snippet and mobile SDKs, and its device fingerprinting is solid, but device intelligence is one input to Sift's scoring, not the core product. If device-level fraud detection is your top priority, Castle provides meaningfully deeper capability.

![Analytics dashboard showing fraud detection rates and transaction risk scoring metrics](https://images.unsplash.com/photo-1551288049-bebda4e38f71?w=800&q=80)

### Rule Customization

Sift offers the most flexible rule engine. You can reference any field from any event type, combine conditions with AND/OR logic, use aggregated metrics (counts, sums, averages over time windows), and trigger different actions per workflow. Stripe Radar's rules engine is powerful but limited to payment-related attributes and Stripe metadata. Castle's policy engine is focused on authentication decisions, which limits its scope but makes it straightforward for its intended use case.

### Cost per Transaction Screened

Stripe Radar's basic ML is free for Stripe users. Radar for Fraud Teams costs $0.07 per screened transaction. For a business processing 100,000 transactions per month, that is $7,000 monthly on top of standard Stripe processing fees. Sift's pricing is contract-based and not publicly listed, but typical contracts for mid-market companies range from $2,000 to $10,000 per month depending on volume and which workflows you deploy. Enterprise deals can exceed $100,000 annually. Castle prices by monthly active users rather than transactions, starting around $500 per month for smaller applications and scaling based on MAU tiers. For applications with high transaction-to-user ratios (repeat purchases, subscriptions), Castle's per-user pricing can be significantly cheaper than per-transaction pricing.

## Integration Architecture and When to Combine Tools

Each tool requires a different integration pattern, and the effort varies significantly depending on your existing stack and what you are trying to protect.

### Stripe Radar Integration

If you use Stripe, Radar is already running. The basic ML model scores every charge automatically. To upgrade to Radar for Fraud Teams, you enable it in the Stripe Dashboard and start writing rules. Integration effort: near zero for existing Stripe users. The rules engine and review queue are available immediately. Custom metadata (order value, product category, customer tenure) can be passed through Stripe's metadata fields to improve ML accuracy and enable more specific rules. Total integration time for a typical Stripe merchant is one to three days.

### Sift Integration

Sift requires instrumenting your application to send events at every relevant touchpoint. You add the Sift JavaScript snippet to your frontend for device signal collection, then send server-side events via Sift's REST API or SDKs whenever a user creates an account, logs in, updates their profile, creates an order, submits a payment, or requests a refund. Each event includes the user's ID, session ID, device token, and whatever custom fields are relevant to the action.

For a typical SaaS application, full Sift integration takes two to six weeks depending on the number of event types you need to instrument and the complexity of your backend architecture. Sift provides SDKs for Python, Ruby, Java, Node.js, and PHP, plus a REST API for languages without an SDK. The upfront investment is significant, but once instrumented, adding new rules and workflows happens in the Sift console without code changes.

### Castle Integration

Castle integration sits between Radar and Sift in complexity. You add Castle's JavaScript or mobile SDK to your frontend for device fingerprinting, then make server-side API calls at authentication and sensitive-action points (login, password change, payment method update, account settings change). The typical integration covers five to ten touchpoints and takes one to two weeks. Castle's API is well-documented and the SDKs cover Ruby, Python, Node.js, PHP, Java, and .NET.

### Combining Tools for Defense in Depth

The strongest fraud prevention architectures use multiple tools at different layers. A common and effective pattern for applications processing significant payment volume:

- **Castle at the account layer:** Device fingerprinting on every session, risk scoring on login and sensitive actions, adaptive MFA to block account takeover before it leads to fraud.

- **Stripe Radar at the payment layer:** ML-based transaction scoring with custom rules for your specific fraud patterns, 3D Secure for high-risk charges, chargeback liability shifting.

- **Sift for cross-platform intelligence (enterprise):** If you operate a marketplace, process payments across multiple gateways, or need content moderation, Sift ties everything together with shared risk signals across the full user journey.

For most startups and growth-stage companies, Castle plus Stripe Radar provides excellent coverage at a reasonable cost. Sift becomes relevant when your fraud team needs a unified console across multiple fraud types or when you outgrow Radar's rules engine for complex, multi-signal decisions. If you are building compliance infrastructure alongside fraud detection, our guide on [SOC 2 for startups](/blog/soc-2-for-startups) covers how these tools fit into a broader security and compliance program.

## Choosing the Right Fraud Detection Stack for Your Application

The right choice depends on your fraud profile, your payment architecture, and your team's capacity to manage fraud operations. Here is a direct recommendation based on common scenarios.

### Choose Stripe Radar If:

You process all payments through Stripe and your primary concern is payment fraud and chargebacks. You want the strongest ML model with zero integration effort. You do not have a dedicated fraud team and want automated decisions with minimal tuning. Radar for Fraud Teams at $0.07 per transaction is the most cost-effective option for pure payment fraud prevention, and the network effect from Stripe's global transaction data gives you ML accuracy that would take years and massive data volume to build independently.

### Choose Sift If:

You operate a marketplace, platform, or large-scale e-commerce business with fraud vectors beyond payments. You need account takeover protection, content moderation, and payment fraud detection in a single platform. You have (or plan to build) a dedicated trust and safety team that will actively manage rules, review queues, and fraud investigations. You process payments through multiple gateways or have a complex order flow that does not map cleanly to Stripe's charge model. Be prepared for enterprise pricing and a multi-week integration project.

### Choose Castle If:

Account takeover is your biggest fraud vector. You run a SaaS application, a financial platform, or any product where compromised accounts lead to significant damage. You want to reduce authentication friction for legitimate users while raising the bar for attackers. You need device intelligence that goes deeper than what payment processors or general fraud tools provide. Castle pairs well with Stripe Radar: let Castle protect accounts and sessions while Radar protects payments.

### The Bottom Line on Fraud Prevention Investment

Every dollar you spend on fraud prevention should return multiples in reduced chargebacks, recovered revenue, and preserved customer trust. The tools have gotten good enough that "we will deal with fraud later" is no longer a defensible position. Later means after you have already lost money, trained fraudsters that your platform is an easy target, and created a backlog of chargebacks that threatens your payment processing relationship.

For teams building new applications with [AI-powered fraud detection](/blog/ai-fraud-detection-for-apps), the smartest move is starting with Stripe Radar on day one (if you use Stripe), adding Castle when you need account-level protection, and evaluating Sift when your fraud operations mature to the point where a unified platform saves your team more time than it costs. Whichever path you take, the integration work pays for itself quickly. If you want help designing a fraud prevention architecture that fits your specific application and risk profile, [book a free strategy call](/get-started) and we will walk through it together.

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*Originally published on [Kanopy Labs](https://kanopylabs.com/blog/stripe-radar-vs-sift-vs-castle)*