The 8 best fraud detection software in 2026
Last updated on July 5, 2026 · 10 min read
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 runs through the 8 best fraud detection software for 2026, from self-serve tools that read the device behind a session to enterprise platforms that decision and guarantee whole transactions. ShieldLabs is first because it is ours, and 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.
The 8 best fraud detection software
1. 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 a persistent device identifier 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 it hands the score and evidence to your own rules rather than making the decision for you. Pricing is a free tier of 5,000 identifications with no credit card, then flat self-serve plans from $99 a month.
Best for: self-serve teams that want an explainable device-and-anonymity signal to feed their own fraud rules. It is the device-and-anonymity signal layer that feeds your own fraud rules.
2. Sift
A long-standing fraud platform that scores payments, signups, and content across a large cross-network dataset. It is enterprise and sales-led, and strong in e-commerce and fintech.
Best for: larger teams that want a network-scale score across multiple fraud types; the common critique is that the single score is hard to inspect.
3. SEON
A fraud platform built on data enrichment, resolving an email or phone into a digital footprint and combining it with device intelligence and custom rules. It has a Starter plan and then moves to sales-led pricing, and is popular in iGaming and fintech.
Best for: teams that want enrichment signals and hands-on rule control alongside device data.
4. Fingerprint
A self-serve device intelligence API, built on the open-source FingerprintJS project, that produces a persistent visitor identifier and a catalog of device signals. It has a free tier and usage-based pricing from $99.
Best for: developer teams that want a battle-tested device identifier to anchor their own detection.
5. Kount
An Equifax fraud platform covering payments, chargebacks, and account protection across e-commerce, combining device and identity signals with a large data network. It is sales-led.
Best for: merchants that want broad payments-fraud coverage inside a single enterprise platform.
6. Signifyd
A commerce-protection platform centered on guarantee-backed decisions: it approves or declines orders and takes on the liability for approved ones that turn out fraudulent. It is enterprise and usage-based.
Best for: retailers that want guarantee-backed approvals and less manual review.
7. Sardine
A risk platform focused on fintech, banking, and crypto, combining device intelligence with case-management workflows for fraud and compliance teams. It is enterprise and sales-led.
Best for: regulated money-movement businesses that need device signals and case management together.
8. Forter
An identity-based decisioning platform that draws on a large merchant network to approve trusted users in real time, built for scale. It is enterprise and sales-led.
Best for: large online retailers and travel brands that want fast, identity-led decisions.
How to choose
The right software follows from where your fraud actually begins. If it starts upstream, with suspicious traffic, fake accounts, or repeat abusers on fresh identities, a self-serve device-and-signal layer catches it early and cheaply. If it starts at the transaction, with chargebacks and disputed orders, a decisioning or guarantee platform earns its keep. Many teams run both: a device signal in the application and a decisioning layer at checkout.
Two questions narrow it fast. First, do you want a signal you act on or a verdict you accept? An explainable score that feeds your own rules keeps control in your system; a managed decision trades control for speed and, sometimes, a liability shift. Second, how big is your team? A lean team is usually better served by a self-serve tool with a free tier and flat pricing than by an enterprise platform that needs a rollout and an analyst to run it.
Sources
- Juniper Research: Online Payment Fraud Losses to Exceed $362 Billion Globally
- Stripe: Fraud scores explained: How businesses assess transaction risk (2026)
- Wikipedia: Fraud detection
- Wikipedia: Device fingerprint
Frequently asked questions
- What is fraud detection software?
- Fraud detection software assesses whether an online action such as a signup, login, or payment is likely to be fraudulent, then returns a risk score or a decision your business acts on. It works by reading context the action itself does not reveal, like the device behind a session, whether the connection is anonymized, and how the behavior compares to known patterns. Tools range from narrow signal layers to full decisioning platforms.
- How does fraud detection software work?
- It collects signals from the session, the device, the network, the identity, and the behavior, then weighs them into a risk read, using rules, statistical models, or both. The result is delivered in real time so your system can approve a clean session, challenge a risky one, or decline an obvious attack. The strongest tools let you inspect which signals drove the score and encode your own rules on top.
- How much does fraud detection software cost?
- It varies widely. Self-serve tools often have a free tier and flat plans starting around $99 a month, while enterprise platforms are sales-led and priced on transaction volume, usually into five figures a year, sometimes with a guarantee fee on approved orders. The pricing model matters as much as the number, so model your expected volume before committing.
- What is the best fraud detection software for a small business?
- A small business is usually best served by a self-serve tool with a free tier and predictable pricing, because it can be integrated and tested without a sales process. A device-and-signal layer that flags fake accounts, multi-accounting, and anonymized traffic covers the most common early-stage fraud, and a chargeback-focused tool can be added later if disputes become the bigger problem.
- Does ShieldLabs detect fraud?
- ShieldLabs detects the device and network signals that fraud depends on, and surfaces patterns like multi-accounting, rather than making the final fraud decision itself. It gives every session a persistent device identifier and a risk score with the named signals, so your own rules decide what to approve, review, or decline. That keeps the verdict in your application, and the free tier covers your first 5,000 identifications.
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