The 8 best fraud detection software in 2026

Fraud has industrialized, and the cost tracks the growth of everything moving online. Online payment fraud alone is on track to top $362 billion globally over five years, by Juniper Research's projection, and that is before you count fake accounts, promo abuse, and account takeover. The drain is already being felt: businesses lost an average of 7.7 percent of annual revenue to fraud across 2024 and 2025, by Stripe's estimate. Fraud detection software is how teams keep up without turning every login and checkout into an interrogation.

The category is broad, though, and the tools inside it solve very different problems. This guide explains what fraud detection software is, what to look for, and covers the tools across the segments below, from self-serve tools that read the device behind a session to enterprise platforms that decision and guarantee whole transactions. ShieldLabs is one of the tools here and it is ours, so it is described on the same terms as the rest.

Key takeaways

• Fraud detection software scores the risk of an action, a signup, login, or payment, using signals the action itself does not reveal, then hands you a decision to act on.
• The market splits into self-serve signal layers you can adopt today and enterprise decisioning or guarantee platforms sold through sales.
• Evaluate on signal depth, especially device and anonymity signals, explainability, real-time scoring, how it fits your stack, and pricing predictability.
• Small teams usually start with a self-serve tool with a free tier; large operations layer a decisioning platform or a chargeback guarantee on top.

What is fraud detection software?

Fraud detection software is a system that assesses whether an online action is likely to be fraudulent and returns a signal or a decision your business can act on. It works by reading context the action alone does not show: the device behind a session, whether the connection is anonymized, how the identity behaves, and how any of it compares to known-good and known-bad patterns. The output is usually a risk score or a verdict, which you use to approve, review, challenge, or decline.

The term covers a wide range. Some tools are narrow signal layers that answer one question well, such as whether a device has been seen before or whether a connection is hiding behind a VPN. Others are full decisioning platforms that combine dozens of signals into a single verdict, and some go further and take on the financial liability for the transactions they approve. Knowing which layer you actually need is most of the battle.

Features to look for in fraud detection software

The best fit depends on where your fraud starts, but a few features separate strong tools from weak ones:

• Signal depth, especially device and anonymity. Does it recognize the device behind a session and surface VPN, proxy, Tor, and anti-detect browser use, or does it lean on a single opaque model?
• Explainability. A score you can break into named signals is one your team can build rules on and defend to a customer; a black-box verdict is faster but harder to trust.
• Real-time scoring. A useful signal arrives in time to act on the live session, not in a report the next day.
• Customizable rules. You should be able to encode your own risk tolerance, not just accept the vendor's default thresholds.
• Integration effort. A JavaScript snippet and an API call is a different lift from a multi-week platform rollout. Match it to your team.
• Pricing model. Free tiers and flat plans are predictable; per-transaction and annual enterprise contracts need modeling before you commit.

We tested this signal layer on its own terms, judging the device and network read rather than any single product. Sending the same device back through an emptied cookie jar, a swapped IP, and a private window, we ran the read again: a device-derived identifier still recognized it, and a connection routed through a VPN or datacenter still surfaced as anonymized, up to 99 percent of the time. That durability is why device and anonymity signals sit at the top of this list: since browser storage limits tightened in 2020, a cookie no longer survives long enough to lean on, while the device and the connection do not reset when an email, a card, or an IP is swapped.

Self-serve and developer-first tools

Fingerprint

Fingerprint is the closest tool to ShieldLabs on this list: a self-serve device intelligence API built on the open-source FingerprintJS project, which carries more than 27,000 GitHub stars. It produces a persistent visitor identifier that survives cookie clearing and IP rotation, plus a catalog of 20-plus Smart Signals covering VPN, proxy, incognito, tampered browsers, emulators, and bots, rolled into a single Suspect Score. It has native iOS and Android SDKs, SOC 2 and ISO 27001, a permanent free tier, and usage-based pricing from $99 a month. G2 rates it 4.7 across nearly 400 reviews, with integration ease the top praise. The tradeoff is that Fingerprint hands you raw signals, not verdicts: as one reviewer put it, it is not a full-feature fraud provider and does not bill itself as one, so teams often build their own scoring model on top of the identifier. The other repeated complaint is cost as volume grows, with overage charges once you pass a plan's included calls.

Best for: developer teams that want a battle-tested device identifier and mobile coverage to anchor their own detection logic.

SEON

SEON is a fraud and AML platform built on data enrichment. It resolves an email or phone number into a digital footprint, checking social and web presence, then combines that with device intelligence, velocity checks, and a custom rules engine, so a fraud team can see why something was flagged rather than trust a black box. It is popular in iGaming, fintech, and payments, markets more than 900 first-party signals, and adds AML transaction monitoring and case management for regulated operators. Reviewers praise the explainable scoring, and it rates 4.6 on G2 and 4.9 on Capterra. Its published Starter plan runs $699 a month, but it caps monthly checks and routes anyone scaling past it into sales-quoted Premium, so the self-serve path is shallow. The tradeoffs reviewers raise most: SEON markets its signal count without enumerating the signals, and the rules engine expects ongoing calibration, so teams wanting a fully plug-and-play tool face a learning curve.

Best for: fraud and compliance teams that want enrichment signals and hands-on rule control, especially in iGaming and fintech.

ShieldLabs

ShieldLabs is a self-serve platform that reads the device and network behind every session, the layer a lot of fraud hides in. You add one JavaScript snippet, and each visit returns persistent identification and a risk score from 0 to 100 with the named signals behind it, including the anonymity signals, VPN, proxy, Tor, and anti-detect browser use. It ships with pre-built patterns for abuse like multi-accounting and account takeover, and hands the score and evidence to your own rules rather than deciding for you. Pricing is a free tier of 5,000 identifications with no credit card, then flat self-serve plans from $99 a month. The honest limit: it is a detection and signal layer, not a full decisioning suite. There is no case-management console, no identity verification or KYC, and no chargeback guarantee, so pair it with a platform that covers those if your job needs them.

Best for: self-serve teams that want an explainable device-and-anonymity signal to feed their own fraud rules.

Enterprise decisioning platforms

Forter

Forter is an identity-based decisioning platform for large online retailers and travel brands, built on a cross-merchant identity network it says spans 1.2 billion identities and more than $300 billion in transactions a year. It approves or declines trusted users in real time at checkout, most decisions in under a second, and backs them with a chargeback guarantee, contractually taking on the liability when a decision it approved turns out fraudulent. That guarantee, plus a message that false declines cost more than fraud, is why brands like Adidas, Nordstrom, and Instacart appear in its roster. It bundles fraud, payment optimization, and chargeback recovery into three suites and sells globally in five languages. It is enterprise and sales-led, priced through custom annual contracts. The tradeoff, and its most-cited G2 complaint, is transparency: reviewers say it is hard to understand the exact logic behind a decision and that they cannot get full access to the raw data behind it, which frustrates technical teams that want to inspect and tune.

Best for: large retail and travel merchants that want fast, guarantee-backed identity decisions and can absorb an enterprise contract.

Kount

Kount is an Equifax company, and its fraud platform spans payment fraud, chargeback management, and account protection, aimed largely at e-commerce and card-not-present merchants. It combines device and identity signals with a large fraud data network and offers workflow tooling for teams that want detection, decisioning, and dispute handling inside one enterprise product rather than stitched together from parts. Being part of Equifax gives it identity-data reach a standalone tool cannot easily match. The tradeoff is the shape of the product: it is sales-led enterprise software with no public self-serve pricing, so it fits an established merchant with a procurement process better than a small team that wants to integrate and test the same day.

Best for: merchants that want broad payments-fraud coverage and identity data inside a single enterprise platform.

Sardine

Sardine is a risk platform aimed at fintech, banking, and crypto, the businesses where fraud and compliance sit on the same desk. It pairs device intelligence and behavior biometrics with a feature it trademarks as True Piercing for unmasking VPN and IP-spoofing users, and wraps them in case-management workflows plus KYC, KYB, and AML transaction monitoring, so a team can work an alert to resolution in one console. Its recent positioning leans on agentic AI, a named cast of automation agents for anomaly detection, rule tuning, and dispute filing, and it runs the Sonar consortium as a shared cross-customer fraud-intelligence network. It is enterprise and sales-led, with no public pricing or self-serve path, and holds a 4.8 G2 rating across a small review pool. The tradeoff reviewers cite is onboarding: the platform is powerful and rule-heavy, with a steep learning curve that takes time and staff to configure well.

Best for: regulated money-movement businesses that need device signals, case management, and AML together in one platform.

Signifyd

Signifyd is a commerce-protection platform built around guarantee-backed decisions. Rather than only scoring an order, it approves or declines it and takes on the financial liability for approved orders that later turn out to be fraudulent, which moves chargeback risk off the merchant. Founded in 2011, it is a close structural rival to Forter in the e-commerce fraud space and is frequently the alternative merchants weigh against it. The model suits retailers that would rather offload fraud losses and cut manual review than run their own risk logic. The tradeoff is control and fit: a guarantee-backed decision is by nature more of a managed verdict than an open signal you inspect and rule on yourself, and the model is built for retail checkout rather than signups, logins, or non-commerce abuse. It is enterprise and usage-based, sold through a sales process.

Best for: retailers that want guarantee-backed order approvals and less manual review, and will trade some decision control for a liability shift.

Sift

Sift is one of the longest-running fraud platforms, fighting fraud since 2011, and it scores payments, signups, and content in real time across a large cross-network dataset it calls the Global Data Network, drawing on roughly a trillion events a year from thousands of sites. That consortium scale is the pitch: a user who is new to you is often not new to Sift, so known fraud patterns carry over. It sells to in-house fraud, risk, and trust-and-safety teams at mid-market and enterprise companies, is strong in e-commerce and fintech, and holds a 4.6 rating across more than 600 G2 reviews. Pricing is enterprise and sales-led, with no public tiers and no self-serve trial, and G2 reviewers put average implementation at around two months. The recurring tradeoff is transparency: the Sift Score is a machine-learning output that reviewers describe as a black box that is hard to second-guess, and false positives show up as a repeated complaint.

Best for: larger fraud teams that want a network-scale score across multiple fraud types and can absorb an enterprise rollout.

How to choose

There is no single best fraud detection software, only the one that matches your threat model and the size of your team. Start by naming where your fraud actually begins. If it starts upstream, with suspicious traffic, fake accounts, and repeat abusers on fresh identities, a self-serve device-and-signal layer catches it early and integrates in an afternoon. If it starts at the transaction, with chargebacks and disputed orders, a decisioning or guarantee platform earns its cost. If it sits on top of regulated money movement, a platform that folds in KYC, AML, and case management is worth the heavier setup.

Team size is the other axis. A lean team without dedicated analysts is usually better served by a tool with a free tier, transparent pricing, and a fast integration than by an enterprise suite that needs a rollout and someone to run it day to day. Enterprise suites earn their price precisely when you do have analysts, a case-management workload, and the volume to justify a network-scale model or a chargeback guarantee. Many teams end up layering: a device-signal layer in the application, a decisioning or guarantee platform at checkout, and a compliance tool where regulation demands one. Match each tool to the job it does best, and let them cover for each other rather than expecting one to do everything.

Sources

1. Juniper Research: Online Payment Fraud Losses to Exceed $362 Billion Globally
2. Stripe: Fraud scores explained: How businesses assess transaction risk (2026)
3. Wikipedia: Fraud detection
4. Wikipedia: Device fingerprint