The fraud detection software market is under pressure from rising losses and faster attacks. Americans reported losing $15.9 billion to fraud in 2025, up from over $12 billion in 2024.
Losses reported to the FBI’s Internet Crime Complaint Center (IC3) reached $20.9 billion, up 26% in a year and 76% of U.S. organizations faced attempted or actual payment fraud in 2025.
This guide compares 8 fraud detection tools and companies, and shows where SEON fits.
Key Takeaways
How Fraud Detection Software Works
Fraud detection software is technology that scores users, devices and transactions in real time to spot fraud such as fake accounts, account takeovers, bonus abuse and payment fraud.
Rather than judging one data point, it checks whether the evidence around a user, from email and phone history to device setup, network and behavior, tells a consistent story, then approves, reviews or blocks the action.
Most tools combine three layers:
- Real-time decisioning: actions at signup, login and payment, before money moves
- Data signals: digital footprint, device and behavioral data that test whether an identity is real and whether a human is in control
- Detection logic: rules and machine learning that turn those signals into a risk score
Top Fraud Detection Software Companies
Disclaimer: All information is based on publicly available sources and vendor documentation, checked in November 2026. We haven’t tested every tool firsthand. SEON is our product and appears first, the other seven are listed in no ranked order and described in the same format. Spot something outdated? Let us know here, and we’ll review it.
SEON
SEON is a fraud prevention platform that began by addressing fraud in the crypto space and now serves companies including Revolut, Lottoland and ViaBill. It scores risk using first-party data it collects itself, combining digital footprint analysis, device intelligence, behavioral intelligence and AI agent detection.
SEON enriches and scores risk in real time with 1,100+ first-party signals across the customer lifecycle, from pre-onboarding through payments, transaction monitoring and regulatory reporting. Teams can use rules, AI scoring or both, see the signals behind every score and test rule changes against historical transactions before they go live.
The company reports more than $300 billion in fraud prevented and holds over 300 reviews on G2, Capterra and the AWS Marketplace.
Key capabilities include explainable rules and AI scoring, real-time digital footprint and device intelligence, single-API integration and combined fraud and AML coverage.
Read More About SEON’s Reviews on G2
Feedzai
Feedzai is a fraud detection solution targeting banks and PSPs, combining AI-based risk assessment, behavioral analytics, and explainable decisioning. In 2025 the company acquired Demyst to expand its access to external data for fraud detection and customer onboarding. Its RiskOps product integrates identity, credit, and behavioral signals into a unified fraud and AML offering.
Key capabilities include contextual intelligence across identity, credit, and behavior, combined fraud and AML coverage, and scalable deployment for financial institutions.
TruValidate
After acquiring Iovation, is TransUnion’s fraud prevention offering, formed after the 2018 acquisition and rebranding of iovation. It combines device recognition, identity verification, and transaction analysis to provide multi-layered fraud detection, linking device identifiers, proprietary data, and online behavior to assess risk.
Key capabilities include device intelligence, identity and transaction analysis, and integrations across finance, insurance, and tenant screening.
ThreatMetrix
ThreatMetrix, part of LexisNexis Risk Solutions, focuses on user identity verification through behavioral analytics and a shared intelligence network drawing on over 78 billion data points. It is geared toward larger organizations with existing technical infrastructure and integrates with other LexisNexis tools, which suits enterprises already invested in that ecosystem.
Key capabilities include near real-time verification, customizable risk rules, scalable fraud prevention, and global identity intelligence across the digital customer journey.
Sift
Sift offers fraud prevention tools targeting retail, food and beverage, travel, and ticketing industries. Its Digital Trust & Safety Suite covers the customer journey through real-time decisioning and dynamic friction capabilities, applying stricter checks only when a transaction looks risky.
Key capabilities include AI-driven fraud prevention, scalable deployment, and dispute management across digital channels.
Emailage
Acquired by LexisNexis in 2020, Emailage is a fraud prevention solution using email intelligence for real-time risk assessment. It combines LexisNexis’ Digital Identity Network with proprietary email data to support fraud scoring across use cases including account opening, card-not-present transactions, guest checkouts, and account management, making email the primary risk signal.
Key capabilities include predictive risk scoring, ML models, and integration with the LexisNexis Digital Identity Network.
Quantexa
Quantexa is a fraud detection and decision intelligence company that connects internal and external data to build entity relationship views for identifying hidden risk. Its contextual approach supports fraud detection across banking, insurance, and government sectors.
Key capabilities include network analytics, AI-driven entity resolution, and flexible rules engine. The approach is aimed at reducing false positives and accelerating fraud investigations.
Kount
Acquired by Equifax in 2021, Kount is a fraud prevention solution combining AI-based decisioning with device fingerprinting, IP tracking, transaction scoring, and real-time analytics. It draws on Equifax identity data to inform its risk decisions.
The product suite includes Kount 360 for payment risk, Command for card-not-present fraud, Central for workflow management, and Control for account takeover protection, alongside chargeback management tools.
Key capabilities include AI-driven fraud detection, device fingerprinting, IP tracking, transaction scoring, chargeback management, and ecommerce integrations.
Fraud Detection for High-Risk Industries
The fraud a business faces depends on what it sells. Betting operators lose money to bonus abuse and multi-accounting, while payment providers face chargeback schemes spread across merchants. Lenders deal with identity fraud and fake registrations before a loan is ever approved.
High-Risk Industries Compared
| Industry | Common fraud | Where it hits | Software must | SEON case study |
|---|---|---|---|---|
| iGaming | Bonus abuse, multi-accounting, stolen cards | Signup and deposit | Link duplicate accounts at signup | Lottoland, Soft2Bet |
| Payment service providers (PSPs) | Chargeback schemes, payment fraud across merchants | Payment | Score transactions in real time and link repeat attempts across merchants | Payop |
| Lending and buy now, pay later (BNPL) | Identity fraud, fraudulent registrations, chargebacks | Signup and payment | Screen registrations and payments with custom and velocity rules | ViaBill |
| Digital banks | Account takeover (ATO), identity fraud | Onboarding, login and transfers | Feed digital footprint and device signals into risk models | Revolut |
| Marketplaces | Fake listings, fraudulent registrations, ATO | Onboarding, login and listing | Enrich each event with device and network data to automate routine decisions | Swiss Marketplace Group |
What the Data Says
According to SEON’s 2026 Fraud & AML Leaders Report, a survey of 1,010 fraud, risk and compliance leaders:
- The UK confirms the trend: more than 78,000 account takeover cases were reported in 2025, 18% of all fraud filings and up 6% on 2024 according to Cifas.
- Takeovers top the threat list: 26% name account takeover as their top fraud threat.
- Promo and return fraud tie for second: 18% name promo or discount abuse, and 18% name return fraud.
- Speed matters: only 10% go live in under two weeks, while 24% take four months or more.
“If an attacker breaches an account, their first move is to quietly alter notification settings and contact details, obfuscating their tracks before the genuine user realizes what has happened.”
Oliver Bognar, Senior Fraud Consultant
Why it matters: in iGaming, payments and digital banking, every week of rollout is a week of exposure, so pick tools built for your fraud types that go live fast.
Results From SEON Customers
- Lottoland (iGaming): 190% more multi-accounting caught at signup and 50 investigation hours saved weekly
- Soft2Bet (iGaming): 40% fewer manual queries and 20% faster manual reviews
- ViaBill (lending and BNPL): 90% fewer fraudulent registrations and 50% fewer chargebacks
- Rebtel (telecom): 30% fewer ATO attacks and 50% fewer manual login reviews
How to Choose a Fraud Detection Tool
The right tool fits the fraud you face today and keeps working as attackers change tactics. Weigh these factors against your own risks and infrastructure:
- Fraud coverage: Match the tool to your most pressing threats, such as fake accounts, payment fraud or account takeover, and check that it scores sign-ups and logins as well as payments.
- Bot and AI agent detection: Ask whether it flags bots, emulators, device farms and AI agents, since automated attacks scale faster than manual review can keep up.
- Data sources: Find out whether the vendor collects its own data in real time or relies on consortium and third-party data, which can only recognize fraud that someone has already reported.
- Explainability and testing: Choose a tool that shows the signals behind each score and lets you backtest rule changes on your own historical data before they go live.
- Integration flexibility: Most fraud detection tools are offered as software as a service (SaaS), but your infrastructure or security requirements may call for on-premise deployment. Ask how long implementation typically takes.
- Payment model: Look for a trial or short-term option before committing to a long contract, and check that volume-based pricing won’t push you to skip checks on transactions you need to screen.
- Support and documentation: Confirm the vendor offers hands-on help during integration and clear documentation, so your team can resolve issues without waiting on support.
Balancing these factors against your own fraud risks and infrastructure helps you choose a solution that fits your needs rather than the broadest feature set.
Still deciding whether to build or buy your fraud solution? This guide compares costs, scalability and long-term ROI to help you choose with confidence.
Download Free Guide
Frequently Asked Questions
What data does a fraud score pull from?
It depends on the vendor. Most scores combine the data you send, such as email, phone, IP, device and transaction details, with enrichment the vendor adds. SEON, for example, enriches each event with digital footprint, device and behavioral signals it collects itself, so the score reflects whether the whole identity holds together.
Do you get the raw data or just a risk score?
Ask before you buy. Some vendors return only a score, while others return the rules that fired, the machine learning reasons and the raw enrichment behind them. SEON returns all three in its API response, so analysts can audit decisions and build their own rules.
Is device monitoring a one-time check, or does it run through the session?
Basic tools check the device once, at login or checkout. Session monitoring follows the whole journey, flagging copy-paste, long pauses, remote access or a user on an active call before a sensitive action such as a payment or a change to bank details.
Can fraud detection software stop AI-driven fraud like bots and deepfakes?
Yes, if it looks beyond the transaction. AI fraud detection software should flag bots, emulators, cloud phones and AI agents from device and behavior signals, and use liveness checks to catch deepfakes at onboarding.
How do you test a fraud tool against your current vendor?
Run a backtest on historical data labeled as fraud or legitimate, then a side-by-side pilot on live traffic. Compare fraud caught, false positives and manual review volume at the same approval rate, using your own data rather than vendor averages.
