Best E-commerce Fraud Detection Software

Summary

E-commerce fraud rarely shows up as a single bad transaction. It spans account takeover (ATO), card testing, friendly fraud and chargebacks, promo and refund abuse, and coordinated fraud rings that look legitimate when you evaluate orders in isolation. That’s why the best e-commerce fraud detection software needs to do more than produce a risk score; it should help you connect identities across sessions, act in real time without adding checkout latency, and give fraud teams practical controls to manage approvals, reviews, step-ups, and denials.

In this guide, we compare leading platforms across the main approaches buyers see in the market: platform-first orchestration and entity intelligence, persistent device intelligence, network and consortium scoring, and chargeback-guarantee providers. You’ll see where each tool tends to fit best (fast-growing DTC, marketplaces, enterprise retail), what strengths they’re known for, how pricing is typically packaged (subscription, per-transaction, custom, guarantee-based), and the tradeoffs that matter in production, like explainability, operational workload, and how well each solution extends beyond payments into ATO and policy abuse.

We put FraudNet first because many teams evaluating best-in-class tooling are ultimately trying to unify fraud decisions across the customer lifecycle (transactions, accounts, and entities) while keeping control over strategy as threats evolve. From there, we’ll benchmark FraudNet against other top options, so you can choose the stack that best matches your risk appetite, integration realities, and growth plans.

1. FraudNet

Platform summary: FraudNet is a fraud, risk, and compliance platform built for e-commerce organizations that need real-time scoring, entity-level intelligence, and workflow orchestration across the customer lifecycle, not just checkout. It’s designed to help teams reduce fraud losses and chargebacks while maintaining conversion and retaining policy control as threats evolve.

If you want a product-level overview of how FraudNet supports strategy design, decisioning, and investigations, start with FraudNet fraud detection and prevention.

Key benefits:

• Approve more legitimate orders by reducing false positives and false declines through better identity context and practical decision controls.
• Detect organized fraud (rings, multi-accounting, coordinated abuse) by linking entities and behaviors across events, not just evaluating transactions in isolation.
• Unify fraud, risk, and compliance workflows so alerts, investigations, and outcomes stay connected and auditable across teams.
• Adapt quickly as fraud patterns change using transparent controls (rules plus scoring) rather than relying on a single opaque output.

Core features:

• Real-time transaction monitoring designed to support fast decisions without adding unnecessary checkout friction.
• Intelligent decisioning that supports approvals, reviews, step-ups, and denials based on configurable strategy.
• Entity linking and relationship analysis to surface hidden connections across identities, devices, and behaviors.
• Data orchestration to ingest, enrich, and action signals across systems and channels for a consistent view of risk.

Primary use cases:

• Marketplace fraud and ring detection (multi-accounting, coordinated refund abuse, promotion abuse) where entity linking is critical.
• High-volume, cross-channel monitoring (web, mobile, and account events) that requires consistent decision logic and reporting.
• Fraud and compliance workflow alignment where investigations, outcomes, and reporting need standardized governance.

Recent updates: FraudNet is positioned as a continuously enhanced platform with ongoing refinements to scoring, entity intelligence, network-driven insights, and decision workflows to support faster response as threats evolve.

Setup considerations: FraudNet is a platform approach, so implementation scope can be broader than plug-and-play tools, especially if you want to fully leverage orchestration, data ingestion, and entity modeling. Pricing is typically custom, so teams should validate total cost of ownership alongside expected lift in approval rates, reduced fraud loss, and decreased review workload.

2. Fingerprint

Platform summary: Fingerprint is a device intelligence platform focused on persistent device identity and risk signals that help e-commerce teams recognize repeat visitors and detect high-risk environments with minimal friction. It’s typically used as an input to a broader decisioning stack.

Core features:

• Persistent device identity built from multiple browser, device, and network signals.
• Signals designed to identify risk factors like proxies and VPNs, automation, or browser tampering.
• APIs and SDKs to embed device intelligence into login and checkout flows.
• Flexible signal consumption so teams can route sessions into step-up, review, or automated actions via existing workflows.

Teams evaluating broader coverage for login-layer threats typically benchmark this category alongside account takeover detection software.

Setup considerations: Fingerprint is usually a signal layer rather than an end-to-end platform, so many teams still need orchestration, policies, and case workflows to act on signals. Engineering involvement is typically required to instrument web and mobile flows and route signals into decision logic.

3. Kount

Platform summary: Kount is commonly evaluated for consortium intelligence paired with configurable policy controls. It’s designed to help organizations balance fraud loss, chargebacks, and false declines with hands-on governance.

Core features:

• Consortium signals intended to detect patterns beyond a single merchant’s dataset.
• Real-time scoring paired with configurable policies and thresholds.
• Integrations positioned to reduce time-to-deploy in common payments environments.
• Operational tooling often framed to support fraud workflows end-to-end.

Setup considerations: Value depends heavily on tuning thresholds, rules, and exception handling to match your risk appetite and customer experience goals. Costs can scale with transaction volume in per-transaction models, so forecast spend under peak and growth scenarios.

4. Signifyd

Platform summary: Signifyd is centered on automated order decisions plus chargeback risk transfer via a guarantee model for approved orders. It is often positioned for fast time-to-value through common integrations.

Core features:

• Chargeback guarantee on approved orders (coverage subject to terms).
• Automated order risk assessment used to drive approve or decline decisions.
• Integrations with major e-commerce ecosystems for faster rollout.
• Decision automation designed to reduce manual review burden during high-volume periods.

If you’re comparing risk-transfer models and operational tradeoffs, use chargeback fraud prevention software providers as a cross-check for packaging, coverage considerations, and typical fit.

Setup considerations: Validate what’s covered versus excluded (fraud types, dispute categories, edge cases) to avoid gaps, especially beyond chargebacks like refunds, returns, and promo abuse. Expect custom pricing and model total cost relative to approval strategy, AOV, and expected conversion impact.

5. Riskified

Platform summary: Riskified is an enterprise-oriented provider focused on approval optimization paired with guarantee-backed decisions for approved transactions. It’s typically evaluated by large and global merchants.

Core features:

• Guarantee-backed approvals (subject to terms).
• Decisioning designed for enterprise-scale operations and global complexity.
• Program emphasis on reducing false declines while maintaining controls.
• Support model aligned to high-volume merchant workflows.

Setup considerations: Commercial terms can be complex, so run scenario-based ROI modeling. Confirm coverage scope, exclusions, and operational processes for disputes and representment alignment.

6. Sift

Platform summary: Sift is positioned for coverage beyond checkout by linking identity-level decisions across payments, accounts, and interactions using real-time scoring and network signals, with dispute tooling framed as part of the operational loop.

Core features:

• Identity-level decisioning that connects activity across accounts and transactions.
• Network signals intended to improve detection of emerging and cross-merchant patterns.
• Real-time scoring used to automate high-confidence actions while routing ambiguous cases to review.
• Dispute and chargeback tooling designed to connect decisions with downstream outcomes.

Setup considerations: Expect configuration work to map platform outputs to your e-commerce workflows across checkout, login, refunds and returns, and promotions. Pricing is typically custom, so benchmark against alternatives using a consistent model that includes loss reduction, approval lift, and operational efficiency.

What is E-commerce Fraud Detection Software?

E-commerce fraud detection software is a platform designed to protect online businesses from financial losses and reputational damage caused by fraudulent activity. At its core, it evaluates transactions, account activity, and behavioral signals in real time to assess risk. Depending on the provider, it may combine rules, scoring, device intelligence, velocity checks, and network signals to identify and block threats such as payment fraud, account takeover (ATO), policy abuse, and bot-driven schemes. The goal is to automate and standardize decisioning, approving legitimate orders while flagging or blocking high-risk ones, without creating unnecessary customer friction.

Why is it Important?

In today’s digital marketplace, fraud detection is a fundamental capability for survival and growth. The cost of unchecked fraud extends beyond the initial lost sale to chargeback fees, lost merchandise, and higher operational spend for manual review. Fraud also erodes customer trust; a single account takeover or major abuse wave can damage a brand and increase churn. A strong fraud program protects revenue, reduces operational overhead, and supports a smoother customer journey by minimizing false declines.

How to Choose the Best Software Provider

Selecting the right fraud detection partner requires a methodical evaluation of your business model against a provider’s capabilities. Start by confirming coverage across your threat landscape, including card-not-present fraud, ATO, and policy abuse. Next, evaluate integration realities: the platform should fit your commerce stack and payment flows, and it should scale with your volume and peak events. Finally, assess control and transparency: strong providers balance automation with configurable policies, explainable outcomes, and the operational tooling your team needs to manage reviews, step-ups, and disputes.

Many merchants ground their evaluation by comparing options against common payment-fraud requirements, including the controls outlined in card-not-present fraud detection tools.

Frequently Asked Questions

What types of e-commerce fraud should fraud detection software cover (beyond payment fraud)?

The most effective e-commerce fraud detection software covers the full customer lifecycle, not just checkout. In practice, that means protecting against: (1) account takeover (ATO) and credential stuffing at login, (2) card testing and bot-driven attacks that create downstream chargeback risk, (3) friendly fraud and chargebacks (including dispute operations and evidence workflows), (4) promotion, coupon, and loyalty abuse that erodes margin without always producing chargebacks, (5) refund and return abuse and did-not-receive claims, and (6) coordinated fraud rings using multiple identities, devices, and accounts to appear legitimate in isolated transactions. The differentiator is whether a platform can connect identities and behaviors across sessions, accounts, devices, and orders so patterns emerge before losses accumulate, and whether it supports consistent, auditable decisions across fraud, risk, and compliance stakeholders.

How do I choose between a platform-orchestration solution, a device-intelligence tool, a network-consortium model, and a chargeback guarantee provider?

Choose based on your operating model, risk appetite, and how much control you need over decisions. Platform-first orchestration and entity intelligence solutions are typically best when you need unified decisions across payments, accounts, and policy abuse, and require workflow and audit consistency across teams. Device intelligence tools excel when ATO, bots, and identity resets are major issues and you need durable device-level signals quickly, often as inputs into a broader decision layer. Network and consortium scoring can be useful when you want cross-merchant pattern recognition to augment your own data, especially in new regions or during spikes, but effectiveness depends on tuning policies and thresholds. Chargeback guarantee providers can be a strong fit when you want speed to value and predictable chargeback exposure on approved orders, but you should confirm what’s covered and excluded and whether you also need separate controls for ATO and non-chargeback abuse like promos and refunds.

What data and integrations are typically required, and how long does implementation take?

Implementation requirements vary by product type and your desired scope, but most programs perform best when they ingest both real-time and historical data. Common inputs include: checkout and order data (cart, items, totals, shipping and billing), payment data (authorization results, AVS and CVV, processor response codes), customer and account history (account age, prior purchases, password resets), digital signals (device and browser signals, IP reputation, proxy and VPN indicators, velocity), fulfillment signals (shipping method, delivery confirmation), customer service and refund events, and dispute and chargeback outcomes for feedback loops. Integrations are typically done via APIs, SDKs (web and mobile), event streaming, and connectors to commerce platforms, payment processors, CRM and helpdesk tools, and dispute tools. Timeline depends on complexity: a signals-only device tool may be instrumented in days to weeks, while a platform approach that unifies multiple systems and workflows may take weeks to a few months.

How can fraud detection software reduce false declines without increasing fraud losses?

Reducing false declines requires moving beyond single-point scores and adopting decision strategies that are precise and operationally actionable. Strong programs typically: (1) use identity context to recognize good customers across sessions and channels, (2) apply step-up verification selectively only when risk is elevated, (3) combine scoring with transparent rules and policies so you can encode business context like VIP handling or high-risk shipping lanes, (4) keep decisions fast to avoid checkout latency, and (5) close the loop with outcomes such as chargebacks, refunds, delivery confirmation, and representment results to tune strategy. Measurement should include approval rate, manual review rate, chargeback rate, and customer friction metrics, not just blocked fraud volume.

What compliance and audit capabilities should Compliance Officers and Internal Auditors look for in fraud platforms?

Compliance and audit needs vary by industry and geography, but common requirements include traceability, consistency, and controlled change management. Look for: (1) decision explainability, (2) immutable logging of signals and policy outputs that contributed to each decision, (3) case workflows that document investigations, reviewer actions, and final outcomes, (4) role-based access control and separation of duties for policy changes versus case actions, (5) versioning and change history to support internal governance, and (6) reporting that aligns fraud outcomes with risk and compliance reporting needs. If you operate across multiple channels or brands, also consider whether the platform can standardize policy and reporting without forcing every business unit into a one-size-fits-all approach.

Disclaimer: This article is based exclusively on publicly available information. The tools referenced have not been independently tested by us. Should you identify any inaccuracies or wish to provide recommendations, we invite you to contact Fraud.net.